CN107679676A - A kind of city based on numerical simulation is low to influence exploitation Optimal Configuration Method - Google Patents
A kind of city based on numerical simulation is low to influence exploitation Optimal Configuration Method Download PDFInfo
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Abstract
The invention discloses a kind of low influence exploitation Optimal Configuration Method in city based on numerical simulation, comprise the following steps：Using mathematical modeling, numerical simulation obtains hydrology-water quality data；According to above-mentioned hydrology-water quality data, the low influence exploitation optimizing evaluation system in city is established；Determine the indices score in system；With reference to Delphi method and analytic hierarchy process (AHP), the indices weight in system is determined；In conjunction with above-mentioned indices score and weight, screening city is low to influence exploitation optimization scheme.Compared with prior art, instant invention overcomes the defects of Consideration is not comprehensive enough be present in the low influence exploitation in city, consider environment, economy and social factor, by the way of qualitative and quantitative index combination, the objectively low influence development plan in quantitative evaluation city, the low influence development plan in city of environmental cost and benefit optimization is filtered out, a kind of effective technical management instrument is provided for the multiobjective management of sponge city.
Description
Technical field
The present invention relates to a kind of city Optimal Configuration Method, and in particular to a kind of low influence in city based on numerical simulation is opened
Send out Optimal Configuration Method.
Background technology
As urbanization process is accelerated, great variety, city Permeable stratum area occur for urban land use and vegetative coverage
Increase, original natural hydrology cyclic process is destroyed, cause urban waterlogging and non-point pollution to aggravate.
To solve Ecology, water security and water environmental problems, influence exploitation with low for core concept, propose sponge city this
One rain flood management concept.It is low influence exploitation (i.e. Low Impact Development), mainly by biology delay facility,
The measures such as roof greening, Glassed swale, rainwater utilization control runoff caused by heavy rain and pollution, development area is connect as far as possible
Natural hydrologic cycle is bordering on, effectively alleviates urban waterlogging, cuts down urban runoff pollutional load, saving water resource, protects and change
Kind urban ecological environment.
At present, city is low influences the general single consideration water rate control expansion of development plan setting, influences to develop early stage low
Fail to identify the low environment for influenceing exploitation performance, economic and social benefit completely, fail the comprehensive quantitative and qualitative index that combines and carry out
The screening of storm water man- agement scheme, lack objective influence development stimulation low on city, science, reasonably distribute rationally.
For objective, science, reasonably influence exploitation low on city optimize configuration, integrate, consider comprehensively it is each because
Element, the present invention propose a kind of low new method for influenceing exploitation and distributing rationally in city.
The content of the invention
For solve the deficiencies in the prior art, present invention aims at provide one kind consider environment, economy and it is social because
Element, by the way of qualitative and quantitative index combination, objective, science, reasonably screen the low influence exploitation optimization scheme in city
Method.
The technical solution adopted by the present invention is：
A kind of city based on numerical simulation is low to influence exploitation Optimal Configuration Method, comprises the following steps：
(1) mathematical modeling is utilized, numerical simulation obtains hydrology-water quality data；
(2) according to above-mentioned hydrology-water quality data, the low influence exploitation optimizing evaluation system in city is established；
(3) the indices score in system is determined；
(4) Delphi method and analytic hierarchy process (AHP) are combined, determines the indices weight in system；
(5) above-mentioned indices score and weight are combined, screening city is low to influence exploitation optimization scheme.
Mathematical modeling in above-mentioned steps (1) includes SWMM (Storm Water Management Model), ArcGIS
Mathematical modeling；
Numerical simulation obtains hydrology-water quality data：Basic data and parameter are inputted to mathematical modeling, exports hydrology-water quality
Data, including hydrographic data and water quality data；
The basic data and parameter include pollutant concentration, pollutant in rainfall, urban surface Manning coefficient, rainwater
Attenuation coefficient, low influence exploitation species and area；
The hydrographic data includes low influence and develops forward and backward run-off, low to influence to develop forward and backward runoff peak value, low influence
Develop forward and backward outer row's rainfall；Water quality data includes low influence and develops forward and backward SS (suspension), COD (COD), TN
(total nitrogen), TP (total phosphorus).
Mathematical modeling in above-mentioned steps (1) must carry out hydrology-water quality parameter calibration checking：
Using correlation coefficient rxy, relative error RE and Nash-Sutcliffe efficiency factor NSE carry out model result it is reliable
Sex determination, computational methods are as follows：
In formula (1), xiFor measured data,For measured data average value, yiFor the mathematical modeling analogue value,For mathematical modeling
Analogue value average value；
Work as coefficient correlation | rxy|>0.7th, relative error RE<0.3 and Nash-Sutcliffe efficiency factors NSE>When 0.7, recognize
It is reliable for mathematical modeling analog result.
Optimizing evaluation system in above-mentioned steps (2), including destination layer, first class index layer, two-level index layer and three-level index
Layer；
The destination layer is that the low influence exploitation in city is distributed rationally；
The first class index layer includes environmental index, economic indicator and social indicator；
The two-level index layer is made up of some two-level index；Environmental index includes runoff Con trolling index and water correction refers to
Mark, economic indicator include cost savings index and operational effect index, and social indicator utilizes index and life including Rainwater Resources
State Landscape metrics；
The three-level indicator layer is made up of some three-level indexs；Runoff Con trolling index includes runoff volume Con trolling index and footpath
Stream peak value Con trolling index, water correction index, which includes SS, improves index, COD improvement index, TN improvement index, TP improvement indexs,
Cost savings index includes construction cost and saves index and maintenance cost saving index, and operational effect index includes design robustness
Index and operation stability index.
Agriculture products score in above-mentioned steps (3), step are as follows：
A1：Runoff volume Con trolling index, which is assigned, to be divided
The requirement of runoff volume control rate zonal control rate is η where city1≤η≤η2, when runoff volume control rate η meets η
≥η1When, runoff volume Con trolling index assigns 100 points, works as η<η1When, pressAssign and divide, runoff volume control rate η is calculated such as
Under：
In formula (4), η1The minimum limit value of runoff volume control rate zonal control rate where city, η2The runoff where city
Overall control rate zonal control rate highest limit value, H1For low outer row's rainfall after influenceing exploitation, H2For low outer row's rain before influenceing exploitation
Amount, H1And H2In terms of mm；
A2：Runoff peak value Con trolling index, which is assigned, to be divided
For medium and small catchment, as runoff peak value control rate β >=50%, runoff peak value Con trolling index assigns 100 points,
Work as β<When 50%, pressAssign and divide；
For heavy rain, Rainstorms, as runoff peak value control rate β >=10%, runoff peak value Con trolling index assigns 100 points,
Work as β<When 10%, pressAssign and divide, runoff peak value control rate β is calculated as follows：
In formula (5), β is runoff peak value control rate, Q1For low runoff peak value before influenceing exploitation, Q2For low footpath after influenceing exploitation
Stream peak value, Q1And Q2With m3/ s is counted；
A3：Water correction index, which is assigned, divides
Assuming that there are M low influence development plans, the comprehensive score F of each scheme water pollution is arranged, entered first
Row F ＜ 0 ranking, | F | smaller, ranking is more forward, and water correction effect is better；F ＞ 0 ranking is carried out again, and F is smaller, ranking
It is more forward；The scheme being ranked first assigns 100 points, and the last scheme assigns 0 point, and remaining scheme interpolation, which is assigned, divides；
In formula (6), S3iFor the water correction index score value of development plan corresponding to i-th of ranking；
Water pollution comprehensive score F analytical procedures are as follows：
1) significance analysis：It is low on city to influence before and after developing between water quality using Wilcoxon signed rank test methods
Difference carries out significance analysis；
2) water quality classification differentiates：Using improved grey relational analysis method, water quality classification is entered before and after influence exploitation low on city
Row differentiates；
3) pollutant sources are analyzed：Using PCA, water quality parameter progress dimensionality reduction after influenceing exploitation low on city
Processing；
A4：Construction cost, which is saved index and assigned, divides
Construction cost saves index score value S4It is calculated as follows：
In formula (7), Y influences development plan construction cost, Y to be lowminIt is minimum for construction cost in low influence development plan
Value；
A5：Maintenance cost, which is saved index and assigned, divides
Maintenance cost saves index score value S5It is calculated as follows：
In formula (8), W influences development plan maintenance cost, W to be lowminIt is minimum for maintenance cost in low influence development plan
Value；
A6：Design robustness index, which is assigned, divides
The design robustness represents the low matching degree for influenceing development stimulation practical operation situation and design in city；
The low species for influenceing development stimulation includes biology and is detained grid, permeable pavement, concave herbaceous field；
Low influence development stimulation is evaluated using " excellent, more excellent, good, poor, poor " five grades are qualitative, it is " excellent " to assign
100 points, " more excellent " to assign 80 points, " good " to assign 60 points, " poor " to assign 40 points, " poor " to assign 20 points, city is low to influence exploitation design Shandong
Rod index score value S6It is calculated as follows：
In formula (9), AkDevelopment stimulation floor space, A are influenceed for individual event is lowlThe total floor space of development stimulation is influenceed to be low,
S6kDevelopment stimulation design robustness index score value is influenceed for individual event is low, s is low influence development stimulation species number；
A7：Operation stability index, which is assigned, divides
The operation stability represents the low global reliability for influenceing development stimulation, using " excellent, more excellent, good, poor,
Five grades of difference " are qualitative to be evaluated low influence development stimulation, " excellent " to assign 100 points, " more excellent " to assign 80 points, " good " tax 60
Point, " poor " to assign 40 points, " poor " to assign 20 points, city is low to influence developing operation stability indicator score value S7It is calculated as follows：
In formula (10), AkDevelopment stimulation floor space, A are influenceed for individual event is lowlThe total floor space of development stimulation is influenceed to be low,
S7kDevelopment stimulation operation stability index score value is influenceed for individual event is low, s is low influence development stimulation species number；
A8：Rainwater Resources are assigned using index divides
As Rainwater Resources utilization rate ε >=15%, Rainwater Resources assign 100 points using index；Work as ε<When 15%, pressAssign and divide, Rainwater Resources utilization rate ε is calculated as follows：
In formula (11), Q4For using rainwater resource amount, γ is season reduction coefficient every year,For comprehensive runoff coefficient, H3
For Multi-year average precipitation, A is survey region catchment area, H3In terms of m；
In formula (12), ε is Rainwater Resources utilization rate, Q3For average annual rainwater-collecting and the rainwater total amount that utilizes, Q3And Q4
With m3Meter；
The season reduction coefficient γ is to eliminate the influence that non-rainy season rainfall is difficult by；
A9：Ecoscape index, which is assigned, divides
The ecoscape represents low overall space structure, interaction, the coordination work(for influenceing development stimulation and being formed
Can and dynamic change, using " high benefit, more efficient be beneficial, medium benefit, compared with poor benefit, poor benefit, without benefit " six grades determine
Property low influence development stimulation is evaluated, " high benefit " assigns 100 points, and " more efficient benefit " assigns 80 points, and " medium benefit " assigns 60 points,
" compared with poor benefit " assigns 40 points, and " poor benefit " assigns 20 points, and " no benefit " assigns 0 point, and city is low to influence exploitation ecoscape index score value
S9It is calculated as follows：
In formula (13), AkDevelopment stimulation floor space, A are influenceed for individual event is lowlThe total floor space of development stimulation is influenceed to be low,
S9kDevelopment stimulation ecoscape index assignment is influenceed for individual event is low, S is low influence development stimulation species number.
Further, the significance analysis in above-mentioned A3：
Assuming that have M low influence development plans, and it is low to influence have p group water quality parameters in the forward and backward water quality data of exploitation, N is set
The typical rainfall in field, i.e., different precipitation guaranteed rates pr, i.e., it is single it is low influence development plan have N group samples, with it is single it is low influence open
It is low on city to influence to develop forward and backward single water using Wilcoxon signed rank test methods in originating party case exemplified by single water quality parameter
Difference between matter carries out significance analysis, and step is as follows：
B1：Calculate paired data difference
di=| x2,i-x1,i|, i=1,2 ..., N (14)
In formula (14), diInfluence to develop forward and backward water quality data difference to be low, if di=0 is given up, x2,iExploitation is influenceed to be low
Water quality data average value afterwards, x1,iFor low water quality data average value before influenceing exploitation；
In formula (15), sgn (x2,i-x1,i) it is sign function, to judge (x2,i-x1,i) sign；
B2：The order of calculating difference
By diArranged by ascending order, obtain corresponding order Ri；
B3：Calculate test statistics
In formula (16), W+For (x2,i-x1,i) ＞ 0 | x2,i-x1,i| corresponding RiSum, be positive number, NrTo cast out di=0
Sample size afterwards；In formula (17), W-For (x2,i-x1,i) ＜ 0 | x2,i-x1,i| corresponding-RiSum, be negative；
B4：Establish hypothesis testing
In formula (18), MdFor the low Overall median number for influenceing to develop forward and backward water quality data difference, H0Thinking low influences exploitation
The forward and backward positive and negative intensity of variation of water quality data is substantially suitable, and distribution gap is smaller, H1Water quality data is deposited before and after thinking low influence exploitation
In significant difference；
B5：Conspicuousness judges
Take test statistics W=min (W+,|W-|), construct the Z statistics of Normal Distribution：
Using statistical software or look into Wilcoxon signed rank test distribution tables and obtain in H0Probability p value under assuming that, give
Level of significance α, if p≤α, refuse null hypothesis H0, it is believed that before and after low influence exploitation there is significant difference in water quality data, if p>
α, then receive null hypothesis H0, it is believed that water quality data is without significant difference before and after low influence exploitation.
Further, the water quality classification in above-mentioned A3 differentiates：
With《Water environment quality standard (GB3838-2002)》Water quality classification is used as with reference to rank, using improved grey model
Correlation fractal dimension, the forward and backward water quality classification of exploitation that influences low on city differentiate that step is as follows：
C1：Variable nondimensionalization
Assuming that have M low influence development plans, and it is low to influence have p group water quality parameters in the forward and backward water quality data of exploitation, N is set
The typical rainfall in field, i.e., different precipitation guaranteed rates pr, i.e., it is single it is low influence development plan have N group samples, with it is single it is low influence open
Exemplified by originating party case, matrix [x' is formedjk]N×p, the low influence obtained on mathematical modeling using Maximum Approach develops forward and backward water quality
Data carry out nondimensionalization processing, obtain matrix [xjk]N×p：
In formula (21), x 'jkFor original water quality data, xjkFor water quality data after nondimensionalization, maxx'kJoin for same water quality
Several lower V class water quality classification maximums；
C2：It is determined that analysis ordered series of numbers
By taking single game typical case's rainfall as an example, it is determined that analysis ordered series of numbers：
X0={ x0(k) | k=1,2 ..., p } j=0 (22)
Xj={ xj(k) | k=1,2 ..., p } j=1,2 ..., m (23)
In formula (22), X0For reference sequence (female ordered series of numbers), influence to develop forward and backward water quality as reference sequence using low；
In formula (23), XjTo compare ordered series of numbers (subnumber row), it is using water quality assessment standard of all categories as ordered series of numbers, m is compared《Ground
Table quality standard of water environment (GB3838-2002)》Water quality classification number (I class, II class, III class, IV class and V class, i.e. m=5)；
C3：Calculate correlation coefficient
In formula (24), Δj(k) it is k-th of water quality assessment index X0With XjAbsolute difference, aj(k) it is water quality data xj(k)
The upper limit, bj(k) it is water quality data xj(k) lower limit；
In formula (25), ζj(k) it is incidence coefficient, P is resolution ratio, and interval is P=(0,1), and P is smaller, resolving power
It is bigger；
C4：Calculating correlation
Low influence is calculated using mean value method and develops water quality data and water quality assessment of all categories under forward and backward single game typical case rainfall
The degree of association of standard：
In formula (26), κjInfluence to develop water quality data and water quality assessment mark of all categories under forward and backward single game typical case rainfall to be low
The accurate degree of association；
C5：Degree of association size sorts
The degree of association directly reflects good and bad relation of each comparative sequences for reference sequences, and degree of association descending is arranged, obtained
To grey relational order, so that it is determined that low influence to develop forward and backward water quality classification.
Further, the pollutant sources analysis in above-mentioned A3：
Using PCA, water quality parameter carries out dimension-reduction treatment after influence exploitation low on city, and step is as follows：
D1：Variable nondimensionalization
Assuming that have M low influence development plans, and it is low to influence have p water quality parameter in water quality data after developing, N fields allusion quotation is set
Type rainfall, i.e., different precipitation guaranteed rates pr, i.e., single low influence development plan has a N group samples, the individual low influence development plans of M
There are R=M × N group samples, form matrix [x 'ij]R×p, the low influence that mathematical modeling obtains is opened using Z-score Standardization Acts
Water quality data after hair carries out nondimensionalization processing, obtains normalized matrix [xij]R×p：
In formula (27),For jth row water quality data average value；
In formula (28), sjFor jth row water quality data standard deviation；
In formula (29), xijNondimensionalization water quality data after being standardized for water quality data Z-score；
D2：Calculate covariance matrix
Covariance matrix (correlation matrix) J=[sij]p×p：
In formula (30), sijFor water quality data XiAnd XjCoefficient correlation,For the i-th row water quality data average value；
D3：Calculate characteristic value and characteristic vector
Characteristic equation is solved with Jacobi methods | λ I-J |=0, obtain eigenvalue λi(i=1,2 ..., p, λ is arranged in descending order1
≥λ2≥…≥λp) and characteristic value corresponding to orthogonalization unit character vector ei(i=1,2 ..., p), eijRepresent vectorial eiJth
Individual component；
D4：Calculate principal component contributor rate and contribution rate of accumulative total
In formula (31), ρiFor principal component contributor rate；
In formula (32), G (g) is contribution rate of accumulative total, as G (g) >=85%, it is believed that can reflect the information of raw water qualitative change amount, g
For the principal component number of final choice；
D5：Calculate principal component expression formula
Fi=ei1X1+ei2X2+…+eipXp, i=1,2 ..., g (33)
In formula (33), FiFor the principal component of water correction evaluation index, X is the water quality data after nondimensionalization；
In formula (34), F is water pollution comprehensive score, is the quantitative description of water pollution degree.
It is as follows with reference to Delphi method and analytic hierarchy process (AHP), agriculture products weight, step in above-mentioned steps (4)：
E1：Target layers are built
Hi=(environment, economic, society)
Hij=(exemplified by two-level index corresponding to " environment " first class index)=(footpath flow control, water correction)
Hijk=(exemplified by " footpath flow control " three-level index corresponding to two-level index)=(runoff volume, runoff peak value)
(35)
In formula (35), HiFor first class index, HijFor corresponding to HiTwo-level index, HijkFor corresponding to HijThree-level index；
E2：Index judgment matrix is built
Expert understands that city is low to influence exploitation optimizing evaluation system, it is anonymous to target layers construction relative to last layer time certain
For individual index, in this level between index significance level judgment matrix A'；
Assuming that an indicator layer Elements C is criterion above, the next indicator layer element dominated is u1,u2,…un(n is element
Number), if u1,u2,…unThe importance of Elements C can be quantified, weight can be determined directly, conversely, using comparative approach two-by-two,
Judge for Elements C, element uiAnd ujSignificance level, importance degree is assigned according to 1~9 proportion quotiety and divided, obtains judgment matrix
A'=[a 'ij]n×n, wherein a 'ijFor element uiAnd ujRelative to the significance level of Elements C；
E3：Judgment matrix approach is assessed
In formula (36), CI is judgment matrix average value, and n is matrix exponent number；
In formula (37), CR is random Consistency Ratio, and RI is same order mean random number；WhenWhen, judge square
Battle array has satisfied uniformity, on the contrary, it is believed that expert, which assesses, has larger error, readjusts judgment matrix and is allowed to meet unanimously
Property require, obtain A=[aij]n×n；
E4：Relative weighting calculates
Calculate judgment matrix A λmaxAnd its corresponding unit vector, then it is index relative weighting；
Assuming that AiFor i-th of three-level index in low influence development effectiveness System of Comprehensive Evaluation, X, Y, Z is respectively that this refers to
The corresponding one-level of mark, two level, three-level relative weighting, then the index weights are ωi=XYZ；Can calculate by that analogy other three
Level index weights.
City is low in above-mentioned steps (5) influences exploitation optimization scheme screening：
According to the index weights determined in the index score and step (4) determined in step (3), the low shadow in M city is calculated
Development plan score S is rung, score the maximum, which is that city is low, influences exploitation optimization scheme；
In formula (38), S (j), which is that city is low, influences development plan score, SiExploitation indices score is influenceed for city is low,
ωiExploitation three-level index weights are influenceed for city is low.
Wherein, city is low in step (5) influences exploitation optimization scheme screening：According to Urban Rain feature, hydrogeology
The requirement such as feature, runoff pollution situation, waterlogging risk control, with reference to Urban Water Environment outstanding problem, economical rationality sexual factor, choosing
The low index for influenceing exploitation must be up to standard in some cities is selected, if some scheme has any one not up to standard, it is low to be not involved in city
Influence the screening of exploitation optimization scheme.
And the low development plan that influences in city needs to meet：The low water quality data for influenceing to develop forward and backward any water quality parameter in city
Just it may participate in step (5) with significant difference, or the low water quality classification lifting after influenceing exploitation in city, low influence development plan
The low screening for influenceing exploitation optimization scheme in city.
The present invention is advantageous in that：
A kind of city based on numerical simulation of the present invention is low to influence exploitation Optimal Configuration Method, considers environment, warp
Ji and social factor, qualitative, quantitative multi -index is determined, and determined respectively by the way of Delphi method and analytic hierarchy process (AHP) combine
Index weights, objective, science, the low influence development plan in city is reasonably evaluated, filter out environment-cost-efficiency optimization
The low influence development plan in city, overcome in the low influence exploitation in city and the defects of Consideration is not comprehensive enough be present, be sponge city
City's multiobjective management provides a kind of effective technical management instrument.
Brief description of the drawings
Fig. 1 is a kind of low flow chart for influenceing exploitation Optimal Configuration Method in city based on numerical simulation of the present invention；
Fig. 2 is a kind of low structural representation for influenceing exploitation optimizing evaluation system in city based on numerical simulation of the present invention
Figure.
Embodiment
Make specific introduce to the present invention below in conjunction with the drawings and specific embodiments.
As shown in figure 1, the invention discloses a kind of low influence exploitation Optimal Configuration Method in city based on numerical simulation, bag
Include following steps：
(1) mathematical modeling is utilized, numerical simulation obtains hydrology-water quality data；
(2) according to above-mentioned hydrology-water quality data, the low influence exploitation optimizing evaluation system in city is established；
(3) the indices score in system is determined；
(4) Delphi method and analytic hierarchy process (AHP) are combined, determines the indices weight in system；
(5) above-mentioned indices score and weight are combined, screening city is low to influence exploitation optimization scheme.
Numerical simulation in step (1) obtains hydrology-water quality data：Using mathematical modeling, such as SWMM (Storm Water
Management Model), input pollutant concentration, degradation of pollutant system in rainfall, urban surface Manning coefficient, rainwater
Several, low the influence exploitation basic data such as species and area and parameter, export run-off before and after low influence is developed, before low influence exploitation
SS (suspension), COD (change before and after the hydrographic data such as outer row's rainfall and low influence are developed before and after runoff peak value, low influence exploitation afterwards
Learn oxygen demand), TN (total nitrogen), the water quality data such as TP (total phosphorus).
The mathematical modeling that numerical simulation is selected in step (1) must carry out hydrology-water quality parameter calibration checking：Using phase relation
Number rxy, relative error RE and Nash-Sutcliffe efficiency factor NSE carry out model result reliability decision, computational methods are such as
Under：
In formula (1), xiFor measured data,For measured data average value, yiFor the mathematical modeling analogue value,For mathematical modeling
Analogue value average value；
Work as coefficient correlation | rxy|>0.7th, relative error RE<0.3 and Nash-Sutcliffe efficiency factors NSE>When 0.7, recognize
It is reliable for mathematical modeling analog result.
The low influence exploitation optimizing evaluation system in city, including destination layer, first class index layer, two level are established in step (2)
Indicator layer and three-level indicator layer totally four layers of appraisement system.
Destination layer is that the low influence exploitation in city is distributed rationally；
First class index layer includes environmental index, economic indicator and social indicator；
Two-level index layer is made up of two-level index；Environmental index includes runoff Con trolling index and water correction index, economical
Index includes cost savings index and operational effect index, and social indicator is referred to including Rainwater Resources using index and ecoscape
Mark；
Three-level indicator layer is made up of three-level index；Runoff Con trolling index includes runoff volume Con trolling index and runoff peak value control
Index processed, water correction index, which includes SS, improves index, COD improvement index, TN improvement index, TP improvement indexs, cost savings
Index includes construction cost and saves index and maintenance cost saving index, and operational effect index includes design robustness index and fortune
Row stability indicator.
Agriculture products score described in step (3), step are as follows：
A1：Runoff volume Con trolling index, which is assigned, to be divided
The requirement of annual flow overall control rate zonal control rate is η where city1≤η≤η2, with precipitation guaranteed rates Pr=
Exemplified by 80%, when runoff volume control rate η meets η >=η1When, runoff volume Con trolling index assigns 100 points, works as η<η1When, pressAssign and divide, runoff volume control rate η is calculated as follows：
In formula (4), η1The minimum limit value of runoff volume control rate zonal control rate where city, η2The runoff where city
Overall control rate zonal control rate highest limit value, H1For low outer row's rainfall after influenceing exploitation, H2For low outer row's rain before influenceing exploitation
Amount, H1And H2In terms of mm.
A2：Runoff peak value Con trolling index, which is assigned, to be divided
With precipitation guaranteed rates PrExemplified by=80%, medium and small catchment, as runoff peak value control rate β >=50%, runoff
Peak value Con trolling index assigns 100 points, works as β<When 50%, pressAssign and divide, runoff peak value control rate β is calculated as follows：
In formula (5), β is runoff peak value control rate, Q1For low runoff peak value before influenceing exploitation, Q2For low footpath after influenceing exploitation
Stream peak value, Q1And Q2With m3/ s is counted.
A3：Water correction index, which is assigned, divides
Assuming that there are M low influence development plans, the comprehensive score F of each scheme water pollution is arranged, entered first
Row F ＜ 0 ranking, | F | smaller, ranking is more forward, and water correction effect is better；F ＞ 0 ranking is carried out again, and F is smaller, ranking
It is more forward.The scheme being ranked first assigns 100 points, and the last scheme assigns 0 point, and remaining scheme interpolation, which is assigned, divides.
In formula (6), S3iFor the water correction index score value of scheme corresponding to i-th of ranking.
Water pollution comprehensive score F analytical procedures are as follows：
1) significance analysis：It is low on city to influence before and after developing between water quality using Wilcoxon signed rank test methods
Difference carries out significance analysis；
2) water quality classification differentiates：Using improved grey relational analysis method, water quality classification is entered before and after influence exploitation low on city
Row differentiates；
3) pollutant sources are analyzed：Using PCA, water quality parameter progress dimensionality reduction after influenceing exploitation low on city
Processing.
Significance analysis：Assuming that have M low influence development plans, it is low to influence have p group water in (rear) water quality data before developing
Matter parameter, N field typical case's rainfalls, i.e., different precipitation guaranteed rates p are setr, i.e., it is single it is low influence development plan have N group samples, with
In single low influence development plan exemplified by water quality parameter COD, using Wilcoxon signed rank test methods, influence low on city is opened
Difference before and after hair between COD water quality data carries out significance analysis, and step is as follows：
B1：Calculate paired data difference
di=| x2,i-x1,i|, i=1,2 ..., N (14)
In formula (14), diWater quality data difference before and after developing is influenceed to be low, if di=0 is given up, x2,iExploitation is influenceed to be low
Water quality data average value afterwards, x1,iFor low water quality data average value before influenceing exploitation；In formula (15), sgn (x2,i-x1,i) it is symbol
Function, to judge (x2,i-x1,i) sign.
B2：The order of calculating difference
By diArranged by ascending order, obtain corresponding order Ri。
B3：Calculate test statistics
In formula (16), W+For (x2,i-x1,i) ＞ 0 | x2,i-x1,i| corresponding RiAnd be positive number, NrTo cast out di=0
Sample size afterwards；In formula (17), W-For (x2,i-x1,i) ＜ 0 | x2,i-x1,i| corresponding-RiSum, be negative.
B4：Establish hypothesis testing
In formula (18), MdFor the low Overall median number for influenceing water quality data difference before and after developing, H0Thinking low influences before developing
The positive and negative intensity of variation of water quality data is substantially suitable afterwards, and distribution gap is smaller, H1Think low water quality data presence before and after influenceing exploitation
Significant difference.
B5：Conspicuousness judges
Take test statistics W=min (W+,|W-|), construct the Z statistics of approximate Normal Distribution：
Using statistical software or look into Wilcoxon signed rank test distribution tables and obtain in H0Probability p value under assuming that, give
Level of significance α=0.05, if p≤α, refuse null hypothesis H0, it is believed that before and after low influence exploitation there is significance difference in water quality data
It is different, if p>α, then receive null hypothesis H0, it is believed that water quality data is without significant difference before and after low influence exploitation.
Water quality classification differentiates：With《Water environment quality standard (GB3838-2002)》Water quality classification is used as with reference to rank,
Using improved grey relational analysis method, water quality classification differentiates that step is as follows before and after influence exploitation low on city：
C1：Variable nondimensionalization
Assuming that have M low influence development plans, and it is low to influence have p group water quality parameters in (rear) water quality data before developing, N is set
The typical rainfall in field, i.e., different precipitation guaranteed rates pr, i.e., it is single it is low influence development plan have N group samples, with it is single it is low influence open
Exemplified by originating party case, matrix [x' is formedjk]N×p, using Maximum Approach on low (rear) original water quality data progress nothing before influenceing exploitation
Dimensionization processing, obtains matrix [xjk]N×p：
In formula (21), x'jkFor original water quality data, xjkFor water quality data after nondimensionalization, maxx'kJoin for same water quality
Several lower V class water quality classification maximums.
C2：It is determined that analysis ordered series of numbers
With precipitation guaranteed rates PrExemplified by=80%, it is determined that analysis ordered series of numbers：
X0={ x0(k) | k=1,2 ..., p } j=0 (22)
Xj={ xj(k) | k=1,2 ..., p } j=1,2 ..., m (23)
In formula (22), X0For reference sequence (female ordered series of numbers), (rear) water quality is reference sequence before being developed using low influence；Formula (23)
In, XjTo compare ordered series of numbers (subnumber row), it is using water quality assessment standard of all categories as ordered series of numbers, m is compared《Water environment quality standard
(GB3838-2002)》Water quality classification number (I class, II class, III class, IV class and V class, i.e. m=5).
C3：Calculate correlation coefficient
In formula (24), Δj(k) it is k-th of water quality assessment index X0With XjAbsolute difference, aj(k) it is water quality data xj(k)
The upper limit, bj(k) it is water quality data xj(k) lower limit；
In formula (25), ζj(k) it is incidence coefficient, P is resolution ratio, and interval is P=(0,1), and P is smaller, resolving power
It is bigger.
C4：Calculating correlation
Commented using water quality data under (rear) single game typical case's rainfall before the low influence exploitation of mean value method calculating with water quality of all categories
The accurate degree of association of price card：
In formula (26), κjFor water quality data and water quality assessment mark of all categories under (rear) single game typical case's rainfall before low influence exploitation
The accurate degree of association.
C5：Degree of association size sorts
The degree of association directly reflects good and bad relation of each comparative sequences for reference sequences, and degree of association descending is arranged, obtained
To grey relational order, so that it is determined that low (rear) water quality classification before influenceing exploitation.
Pollutant sources are analyzed：It is low on city to influence after developing at water quality parameter progress dimensionality reduction using PCA
Reason, step are as follows：
D1：Variable nondimensionalization
Assuming that have M low influence development plans, and it is low to influence have p water quality parameter in water quality data after developing, N fields allusion quotation is set
Type rainfall, i.e., different precipitation guaranteed rates pr, i.e., single low influence development plan has a N group samples, the individual low influence development plans of M
There are R=M × N group samples, form matrix [x 'ij]R×p, water quality data nondimensionalization processing is carried out using Z-score Standardization Acts,
Obtain normalized matrix [xij]R×p：
In formula (27),For jth row water quality data average value；
In formula (28), sjFor jth row water quality data standard deviation；
In formula (29), xijDimensionless data after being standardized for water quality data Z-score.
D2：Calculate covariance matrix
Covariance matrix (correlation matrix) J=[sij]p×p：
In formula (30), sijFor water quality data XiAnd XjCoefficient correlation,For the i-th row water quality data average value.
D3：Calculate characteristic value and characteristic vector
Characteristic equation is solved with Jacobi methods | λ I-J |=0, obtain eigenvalue λi(i=1,2 ..., p, λ is arranged in descending order1
≥λ2≥…≥λp) and characteristic value corresponding to orthogonalization unit character vector ei(i=1,2 ..., p), eijRepresent vectorial eiJth
Individual component.
D4：Calculate principal component contributor rate and contribution rate of accumulative total
In formula (31), ρiFor principal component contributor rate；
In formula (32), G (g) is contribution rate of accumulative total, as G (g) >=85%, it is believed that can reflect the information of raw water qualitative change amount, g
For the principal component number of final choice.
D5：Calculate principal component expression formula
With precipitation guaranteed rates PrExemplified by=80%.
Fi=ei1X1+ei2X2+…+eipXp, i=1,2 ..., g (33)
In formula (33), FiFor the principal component of water correction evaluation index, X is the water quality data after nondimensionalization；
In formula (34), F is water pollution comprehensive score, is the quantitative description of water pollution degree.
A4：Construction cost, which is saved index and assigned, divides
Construction cost is saved index score value and is calculated as follows：
In formula (7), S4Index score value is saved for construction cost, Y influences development plan construction cost, Y to be lowminFor low shadow
Ring construction cost minimum in development plan.
A5：Maintenance cost, which is saved index and assigned, divides
Maintenance cost is saved index score value and is calculated as follows：
In formula (8), S5Index score value is saved for maintenance cost, W influences development plan maintenance cost, W to be lowminFor low shadow
Ring maintenance cost minimum in development plan.
A6：Design robustness index, which is assigned, divides
Design robustness represents the low matching degree for influenceing development stimulation practical operation situation and design in city, and low influence is opened
The species of hair measure includes biology and is detained grid, permeable pavement, concave herbaceous field, using " excellent, more excellent, good, poor, poor " five
Individual grade is qualitative to be evaluated low influence development stimulation, and " excellent " to assign 100 points, " more excellent " 80 points of tax is " good " to assign 60 points, " compared with
Difference " assigns 40 points, and " poor " to assign 20 points, the low exploitation design robustness index score value that influences in city is calculated as follows：
In formula (9), S6For design robustness index score value, AkDevelopment stimulation floor space, A are influenceed for individual event is lowlTo be low
Influence the total floor space of development stimulation, S6kDevelopment stimulation design robustness index score value is influenceed for individual event is low, s influences to develop to be low
Measure species number.
A7：Operation stability index, which is assigned, divides
Operation stability represents the low global reliability for influenceing development stimulation, using " excellent, more excellent, good, poor, poor " five
Individual grade is qualitative to be evaluated low influence development stimulation, and " excellent " to assign 100 points, " more excellent " 80 points of tax is " good " to assign 60 points, " compared with
Difference " assigns 40 points, and " poor " to assign 20 points, the low developing operation stability indicator score value that influences in city is calculated as follows：
In formula (10), S7For operation stability index score value, AkDevelopment stimulation floor space, A are influenceed for individual event is lowlTo be low
Influence the total floor space of development stimulation, S7kDevelopment stimulation operation stability index score value is influenceed for individual event is low, s influences to develop to be low
Measure species number.
A8：Rainwater Resources are assigned using index divides
As Rainwater Resources utilization rate ε >=15%, Rainwater Resources assign 100 points using index, work as ε<When 15%, pressAssign and divide, Rainwater Resources utilization rate ε is calculated as follows：
In formula (11), Q4For using rainwater resource amount, γ is season reduction coefficient every year,For comprehensive runoff coefficient, H3
For Multi-year average precipitation, A is survey region catchment area, H3In terms of m；
In formula (12), ε is Rainwater Resources utilization rate, Q3Spilt for average annual rainwater-collecting and being poured for road, Garden Greenland
Irrigate, municipal administration is used mixedly, industrial and agricultural production, the rainwater total amount of cooling etc., Q3And Q4With m3Meter；Season, reduction coefficient γ was intended to eliminate
The influence that non-rainy season rainfall is difficult by.
A9：Ecoscape index, which is assigned, divides
Ecoscape represent it is low influence development stimulation formed overall space structure, interaction, coordination function and
Dynamic change, using " high benefit, more efficient be beneficial, medium benefit, compared with poor benefit, poor benefit, without benefit " six grades are qualitative right
Low influence development stimulation is evaluated, and " high benefit " assigns 100 points, and " more efficient benefit " assigns 80 points, and " medium benefit " assigns 60 points, " compared with
Poor benefit " assigns 40 points, and " poor benefit " assigns 20 points, and " no benefit " assigns 0 point, and the low exploitation ecoscape index score value that influences in city calculates
It is as follows：
In formula (13), S9For ecological Landscape metrics score value, AkDevelopment stimulation floor space, A are influenceed for individual event is lowlFor low shadow
Ring development stimulation floor space, S9kDevelopment stimulation ecoscape index assignment is influenceed for individual event is low, s is low influence development stimulation
Species number.
Agriculture products weight in step (4) is as follows with reference to Delphi method and analytic hierarchy process (AHP), step：
E1：Target layers are built
Hi=(environment, economic, society)
Hij=(exemplified by two-level index corresponding to " environment " first class index)=(footpath flow control, water correction)
Hijk=(exemplified by " footpath flow control " three-level index corresponding to two-level index)=(runoff volume, runoff peak value) (35)
In formula (35), HiFor first class index, HijFor corresponding to HiTwo-level index, HijkFor corresponding to HijThree-level index.
E2：Index judgment matrix is built
Expert understands that city is low to influence exploitation optimizing evaluation system, it is anonymous to target layers construction relative to last layer time certain
For individual index, in this level between index significance level judgment matrix A'；
Assuming that an indicator layer Elements C is criterion above, the next indicator layer element dominated is u1,u2,…un(n is element
Number), if u1,u2,…unThe importance of Elements C can be quantified, weight can be determined directly, conversely, using comparative approach two-by-two,
Judge for Elements C, element uiAnd ujSignificance level, importance degree is assigned according to 1~9 proportion quotiety and divided, obtains judgment matrix
A'=[a 'ij]n×n, wherein a 'ijFor element uiAnd ujRelative to the significance level of Elements C；
E3：Judgment matrix approach is assessed
In formula (36), CI is judgment matrix average value, and n is matrix exponent number；
In formula (37), CR is random Consistency Ratio, and RI is same order mean random number；WhenWhen, judge
Matrix has satisfied uniformity, on the contrary, it is believed that expert, which assesses, has larger error, readjusts judgment matrix and is allowed to meet one
Cause property requires to obtain A=[aij]n×n。
E4：Relative weighting calculates
Calculate judgment matrix A λmaxAnd its corresponding unit vector, then it is index relative weighting.
Assuming that AiFor i-th of three-level index in low influence development effectiveness System of Comprehensive Evaluation, X, Y, Z is respectively that this refers to
The corresponding one-level of mark, two level, three-level relative weighting, then the index weights are ωi=XYZ.Can calculate by that analogy other three
Level index weights.
City in step (5) is low to influence exploitation optimization scheme screening, according to the index score of determination in step (3) and
The index weights determined in step (4), the low influence development plan score S in M city is calculated, score the maximum is the low influence in city
Develop optimization scheme.
In formula (38), S (j), which is that city is low, influences development plan score, SiExploitation indices score is influenceed for city is low,
ωiExploitation three-level index weights are influenceed for city is low.
Wherein, city is low in step (5) influences exploitation optimization scheme screening：According to Urban Rain feature, hydrogeology
The requirement such as feature, runoff pollution situation, waterlogging risk control, with reference to Urban Water Environment outstanding problem, economical rationality sexual factor, choosing
The low index for influenceing exploitation must be up to standard in some cities is selected, if some scheme has any one not up to standard, it is low to be not involved in city
Influence the screening of exploitation optimization scheme.
And water correction index is assigned and divided, the low development plan that influences in city meets：The low any water quality before and after influenceing exploitation in city
The water quality data of parameter has the low water quality classification lifting after influenceing exploitation of significant difference or city, and low influence development plan just may be used
Participate in the low screening for influenceing exploitation optimization scheme in city in step (5).
Embodiment 1：
Carry out low influence exploitation by taking a certain urban cells as an example to distribute rationally, step is as follows：
1. numerical simulation obtains hydrology-water quality data
Hydrology-water quality data source calculates in SWMM (Storm Water Management Model) modeling, through rate
Fixed checking, modeling run-off and measured runoff correlation coefficient rxy=0.74, relative error RE=0.28, Nash-
Sutcliffe efficiency factors NSE=0.87；Model is (total to SS (suspension), COD (COD), TN (total nitrogen) and TP
Phosphorus) 4 kinds of pollutants simulation, coefficient correlation is equal | rxy|>0.70, the equal RE of relative error<0.30, Nash-Sutcliffe efficiency system
The equal NSE of number>0.70, it is believed that SWMM model simulation results are reliable.
Hydrology-water quality data are as shown in table 1 before and after low influence exploitation.
The hydrology-water quality data of table 1 (rainfall unit mm；Runoff unit m3/s；Water quality unit mg/L)
2. establishing, city is low to influence exploitation optimizing evaluation system
The low exploitation optimizing evaluation system that influences in city is shown in accompanying drawing 2, including destination layer, first class index layer, two-level index layer and
Three-level indicator layer totally four layers of appraisement system：
Destination layer is that the low influence exploitation in city is distributed rationally；
First class index layer includes environmental index, economic indicator and social indicator；
Two-level index layer is made up of two-level index, and environmental index includes runoff Con trolling index and water correction index, economical
Index includes cost savings index and operational effect index, and social indicator is referred to including Rainwater Resources using index and ecoscape
Mark；
Three-level indicator layer is made up of three-level index, and runoff Con trolling index includes runoff volume Con trolling index and runoff peak value control
Index processed, water correction index, which includes SS (suspension), COD (COD), TN (total nitrogen) and TP (total phosphorus), improves index,
Cost savings index includes construction cost and saves index and maintenance cost saving index, and operational effect index includes design robustness
Index and operation stability index.
3. agriculture products score
(1) runoff volume Con trolling index, which is assigned, divides
By taking a typical rainfall as an example, precipitation guaranteed rates Pr=80%.
Scheme 1：Runoff volume control rate isThe city
The control rate requirement of place runoff volume control partition is 75%≤η≤80%, assigns 100 points.
Scheme 2：η=78.4%, assign 100 points；Scheme 3：η=78.4%, assign 100 points；Scheme 4：η=78.4%, assign 100
Point；Scheme 5：η=78.8%, assign 100 points.
(2) runoff peak value Con trolling index, which is assigned, divides
By taking a typical rainfall as an example, precipitation guaranteed rates Pr=80%, medium and small catchment.
Scheme 1：Runoff peak value control rate isAssign
100 points.
Scheme 2：β=78.4%, assign 100 points；Scheme 3：β=78.4%, assign 100 points；Scheme 4：β=78.4%, assign 100
Point；Scheme 5：β=78.8%, assign 100 points.
(3) water correction index, which is assigned, divides
A. significance analysis
By taking water quality parameter COD in scheme 1 as an example, using Wilcoxon signed rank test methods, significance analysis, structure are carried out
It is as follows to make hypothesis testing：
H0：Significant difference is not present in COD water quality data before and after the low influence exploitation of scheme 1
H1：Before and after the low influence exploitation of scheme 1 there is significant difference in COD water quality data
P is calculated to obtain using statistical softwareCOD=0.012<α (α=0.05), then negate null hypothesis, that is, think that low scheme 1 is low
Before and after influenceing exploitation there is significant difference in COD water quality data, and the water quality parameter significance analysis of scheme 1 is as shown in table 2, it is believed that side
Water quality data is present dramatically different before and after the low influence exploitation of case 1.
The water quality parameter significance analysis (level of significance α=0.05) of 2 scheme of table 1
Similarly, it is believed that water quality data has significant difference before and after the low influence exploitation of scheme 2- schemes 5.
B. water quality classification differentiates
By taking scheme 1 as an example, original water quality data progress nondimensionalization processing before and after developing is influenceed to low using Maximum Approach,
COD water quality data maximums are 40mg/L, and TN water quality datas maximum is 2mg/L, and TP water quality datas maximum is 0.4mg/L,
As shown in table 3.
The processing of the water quality data nondimensionalization of table 3
By taking a typical rainfall as an example, with precipitation guaranteed rates PrWater quality data is reference number before low influence exploitation under=80%
Row, using I class, II class, III class, IV class and V class water quality assessment standard as ordered series of numbers is compared, take P=0.5, water quality data bound is such as
Shown in table 4, low Δ before influenceing exploitationj(k) as shown in table 5, low incidence coefficient and calculation of relationship degree such as table 6 before influenceing exploitation are calculated
It is shown, low water quality and V class water incidence coefficient maximum κ=0.71 before influenceing exploitation.
The water quality data bound (mg/L) of table 4
The low Δ before influenceing exploitation of table 5j(k) calculate
Table 6 low incidence coefficient and calculation of relationship degree before influenceing exploitation
Similarly, with precipitation guaranteed rates PrUnder=80% it is low influence exploitation after water quality data be reference sequence, with I class, II class,
III class, IV class and V class water quality assessment standard take P=0.5, scheme 1 low water quality and IV class water after influenceing exploitation to compare ordered series of numbers
Water quality incidence coefficient maximum κ=0.85, as shown in table 7
7 scheme of table, 1 low calculation of relationship degree after influenceing exploitation
Scheme 2：Low water quality and IV class water incidence coefficient maximum κ=0.85 after influenceing exploitation；
Scheme 3：Low water quality and IV class water incidence coefficient maximum κ=0.86 after influenceing exploitation；
Scheme 4：Low water quality and IV class water incidence coefficient maximum κ=0.85 after influenceing exploitation；
Scheme 5：Low water quality and IV class water incidence coefficient maximum κ=0.85 after influenceing exploitation.
C. pollutant sources are analyzed
Using Z-score Standardization Acts on low original water quality data progress nondimensionalization processing after influenceing exploitation, using master
Componential analysis carries out dimension-reduction treatment to water quality parameter, and water quality assessment index is converted into the water quality overall target of less number,
Principal component contributor rate and accumulation contribution rate are as shown in table 8, according to the principle for choosing principal component number, choose contribution rate of accumulative total and are more than
85%, the first two is taken, is named as principal component F1And F2, principal component coefficient results are as shown in table 9.
The principal component contributor rate of table 8 and accumulation contribution rate
The principal component coefficient results of table 9
Principal component function expression：
F1=e11×X1+e12×X2+e13×X3+e14×X4=0.59 × X1+0.55×X2+0.53×X3+0.25×X4
F2=e21×X1+e22×X2+e23×X3+e24×X4=-0.31 × X1-0.41×X2+0.43×X3+0.74×X4
With precipitation guaranteed rates PrExemplified by=80%, low influence development plan 1- 5 score values of scheme are as shown in table 10.
Each scheme water correction index score value of table 10
(4) construction cost is saved index and assigned and divides
Low influence development stimulation biology is detained grid construction cost with 250 yuan/m2Meter, permeable pavement construction cost is with 120
Member/m2Meter, concave herbaceous field construction cost is with 40 yuan/m2Meter.
The low development ＆ construction cost that influences of scheme 1 is 9462.03 ten thousand yuan,
The low development ＆ construction cost that influences of scheme 2 is 15027.93 ten thousand yuan,
The low development ＆ construction cost that influences of scheme 3 is 12244.98 ten thousand yuan,
The low development ＆ construction cost that influences of scheme 4 is 10575.21 ten thousand yuan,
The low development ＆ construction cost that influences of scheme 5 is 16141.11 ten thousand yuan；
Scheme 1：Construction cost saves index score valuePoint.
Scheme 2：S4=63 points；Scheme 3：S4=77 points；Scheme 4：S4=89 points；Scheme 5：S4=59 points.
(5) maintenance cost is saved index and assigned and divides
Low influence development stimulation biology is detained grid maintenance cost with 15 yuan/m2Meter, permeable pavement maintenance cost is with 10
Member/m2Meter, concave herbaceous field maintenance cost is with 3 yuan/m2Meter.
The low exploitation maintenance cost that influences of scheme 1 is 779.23 ten thousand yuan, and the low exploitation maintenance cost that influences of scheme 2 is 1196.67
Wan Yuan, the low exploitation maintenance cost that influences of scheme 3 is 1057.52 ten thousand yuan, and the low exploitation maintenance cost that influences of scheme 4 is 862.71 ten thousand
Member, the low exploitation maintenance cost that influences of scheme 5 is 1280.16 ten thousand yuan.
Scheme 1：Construction cost saves index score valuePoint.
Scheme 2：S5=65 points；Scheme 3：S5=74 points；Scheme 4：S5=90 points；Scheme 5：S5=61 points.
(6) design robustness index, which is assigned, divides
Design robustness index is qualitative index, and different according to low influence development stimulation, its design robustness is different, according to
The low influence development stimulation that survey region is implemented evaluated by class, as shown in table 11
Table 11 is low to influence the evaluation of development stimulation design robustness
Scheme 1：Design robustness index score valuePoint.
Scheme 2：S6=75 points；Scheme 3：S6=70 points；Scheme 4：S6=55 points；Scheme 5：S6=64 points.
(7) operation stability index, which is assigned, divides
Operation stability index is qualitative index, and different according to low influence development stimulation, its operation stability is different, according to
The low influence development stimulation that survey region is implemented evaluated by class, as shown in table 12.
Table 12 is low to influence the evaluation of development stimulation operation stability
Scheme 1：Operation stability index score valuePoint.
Scheme 2：S7=75 points；Scheme 3：S7=65 points；Scheme 4：S7=80 points；Scheme 5：S7=80 points.
(8) Rainwater Resources are assigned using index and divided
Survey region meter 5565900m2, region location Multi-year average precipitation H3For 1064mm, scheme 1- schemes 5
Average annual rainwater-collecting and the rainwater total amount Q utilized3It is 136500m3, season reduction coefficient γ=0.72 is taken, scheme 1 takes synthesis
Runoff coefficientScheme 2 takes comprehensive runoff coefficientScheme 3 takes comprehensive runoff coefficientScheme 4
Take comprehensive runoff coefficientScheme 5 takes comprehensive runoff coefficient
Scheme 1 is every year using rainwater resource amount
Scheme 1：Rainwater Resources utilization rateAssign 94 points.
Scheme 2：ε=14.7%, assign 98 points；Scheme 3：ε=14.9%, assign 99 points；Scheme 4：ε=14.7%, assign 98 points；
Scheme 5：ε=15.4%, assign 100 points.
(9) ecoscape index, which is assigned, divides
Ecoscape index is qualitative index, and different according to low influence development stimulation, its ecoscape effect is different, according to
The low influence development stimulation that survey region is implemented evaluated by class, as shown in table 13.
Table 13 is low to influence the evaluation of development stimulation ecoscape
Scheme 1：Ecoscape index score valuePoint.
Scheme 2：S9=65 points；Scheme 3：S9=55 points；Scheme 4：S9=60 points；Scheme 5：S9=64 points.
4. determine each index weights
So that first class index assigns power as an example, Hi=(environment, economic, society), structure first class index judgment matrix A1,
Matrix A1λmax=1.425,
Matrix A1Meet coherence request, weight is (environment, economic, society)=(0.659,0.156,0.185), and index weights are distributed
As shown in table 14.
The index weights of table 14 are distributed
5. city is low to influence exploitation optimization scheme screening
Runoff volume Con trolling index is chosen to be satisfied by requiring for necessary index up to standard, each scheme；It is again low to influence before developing
Each scheme water quality data has significant difference afterwards, and water quality classification has been lifted, and each scheme both participates in the low influence exploitation in city most
Prioritization scheme screens.
According to each index score value and weight, each scheme points are calculated, as shown in Table 15, scheme 5, which is that city is low, influences exploitation
Optimal water allocation scheme.
15 each scheme points of table
The basic principles, principal features and advantages of the present invention have been shown and described above.The technical staff of the industry should
Understand, the invention is not limited in any way for above-described embodiment, all to be obtained by the way of equivalent substitution or equivalent transformation
Technical scheme, all fall within protection scope of the present invention.
Claims (10)
1. a kind of city based on numerical simulation is low to influence exploitation Optimal Configuration Method, it is characterised in that comprises the following steps：
(1) mathematical modeling is utilized, numerical simulation obtains hydrology-water quality data；
(2) according to above-mentioned hydrology-water quality data, the low influence exploitation optimizing evaluation system in city is established；
(3) the indices score in system is determined；
(4) Delphi method and analytic hierarchy process (AHP) are combined, determines the indices weight in system；
(5) above-mentioned indices score and weight are combined, screening city is low to influence exploitation optimization scheme.
2. a kind of city based on numerical simulation according to claim 1 is low to influence exploitation Optimal Configuration Method, its feature
It is, the mathematical modeling in the step (1) includes SWMM, ArcGIS mathematical modeling,
Numerical simulation obtains hydrology-water quality data：Basic data and parameter are inputted to mathematical modeling, exports hydrology-water quality data,
Including hydrographic data and water quality data；
The basic data and parameter include pollutant concentration, degradation of pollutant in rainfall, urban surface Manning coefficient, rainwater
Coefficient, low influence exploitation species and area；
The hydrographic data includes low influence and develops forward and backward run-off, low to influence to develop forward and backward runoff peak value, low to influence to develop
Forward and backward outer row's rainfall；Water quality data includes low influence and develops forward and backward SS, COD, TN, TP.
3. a kind of city based on numerical simulation according to claim 1 is low to influence exploitation Optimal Configuration Method, its feature
It is, the mathematical modeling in the step (1) must carry out hydrology-water quality parameter calibration checking：
Using correlation coefficient rxy, relative error RE and Nash-Sutcliffe efficiency factor NSE carry out model result reliability sentence
Fixed, computational methods are as follows：
In formula (1), xiFor measured data,For measured data average value, yiFor the mathematical modeling analogue value,Simulated for mathematical modeling
It is worth average value；
Work as coefficient correlation | rxy|>0.7th, relative error RE<0.3 and Nash-Sutcliffe efficiency factors NSE>When 0.7, it is believed that number
It is reliable to learn model simulation results.
4. a kind of city based on numerical simulation according to claim 1 is low to influence exploitation Optimal Configuration Method, its feature
It is, optimizing evaluation system in the step (2), including destination layer, first class index layer, two-level index layer and three-level indicator layer；
The destination layer is that the low influence exploitation in city is distributed rationally；
The first class index layer includes environmental index, economic indicator and social indicator；
The two-level index layer is made up of some two-level index；Environmental index includes runoff Con trolling index and water correction index,
Economic indicator includes cost savings index and operational effect index, and social indicator utilizes index and ecological landscape including Rainwater Resources
See index；
The three-level indicator layer is made up of some three-level indexs；Runoff Con trolling index includes runoff volume Con trolling index and footpath stream peak
It is worth Con trolling index, water correction index includes SS improvement index, COD improves index, TN improves index, TP improves index, cost
Saving index includes construction cost saving index and maintenance cost saving index, and operational effect index includes design robustness index
With operation stability index.
5. a kind of city based on numerical simulation according to claim 1 is low to influence exploitation Optimal Configuration Method, its feature
It is, agriculture products score in the step (3), step is as follows：
A1：Runoff volume Con trolling index, which is assigned, to be divided
The requirement of runoff volume control rate zonal control rate is η where city1≤η≤η2, when runoff volume control rate η meets η >=η1
When, runoff volume Con trolling index assigns 100 points, works as η<η1When, pressAssign and divide, runoff volume control rate η is calculated as follows：
In formula (4), η1The minimum limit value of runoff volume control rate zonal control rate where city, η2The runoff volume where city
Control rate zonal control rate highest limit value, H1For low outer row's rainfall after influenceing exploitation, H2For low outer row's rainfall before influenceing exploitation, H1
And H2In terms of mm；
A2：Runoff peak value Con trolling index, which is assigned, to be divided
For medium and small catchment, as runoff peak value control rate β >=50%, runoff peak value Con trolling index assigns 100 points, works as β<
When 50%, pressAssign and divide；
For heavy rain, Rainstorms, as runoff peak value control rate β >=10%, runoff peak value Con trolling index assigns 100 points, works as β<
When 10%, pressAssign and divide, runoff peak value control rate β is calculated as follows：
In formula (5), β is runoff peak value control rate, Q1For low runoff peak value before influenceing exploitation, Q2For low footpath stream peak after influenceing exploitation
Value, Q1And Q2With m3/ s is counted；
A3：Water correction index, which is assigned, divides
Assuming that there are M low influence development plans, the comprehensive score F of each scheme water pollution is arranged, carries out F ＜ first
0 ranking, | F | smaller, ranking is more forward, and water correction effect is better；F ＞ 0 ranking is carried out again, and F is smaller, and ranking is more leaned on
Before；The scheme being ranked first assigns 100 points, and the last scheme assigns 0 point, and remaining scheme interpolation, which is assigned, divides；
In formula (6), S3iFor the water correction index score value of development plan corresponding to i-th of ranking；
Water pollution comprehensive score F analytical procedures are as follows：
1) significance analysis：Using Wilcoxon signed rank test methods, the difference influenceed before and after developing between water quality low on city
Carry out significance analysis；
2) water quality classification differentiates：Using improved grey relational analysis method, water quality classification is sentenced before and after influence exploitation low on city
Not；
3) pollutant sources are analyzed：Using PCA, water quality parameter progress dimension-reduction treatment after influenceing exploitation low on city；
A4：Construction cost, which is saved index and assigned, divides
Construction cost saves index score value S4It is calculated as follows：
In formula (7), Y influences development plan construction cost, Y to be lowminFor construction cost minimum in low influence development plan；
A5：Maintenance cost, which is saved index and assigned, divides
Maintenance cost saves index score value S5It is calculated as follows：
In formula (8), W influences development plan maintenance cost, W to be lowminFor maintenance cost minimum in low influence development plan；
A6：Design robustness index, which is assigned, divides
The design robustness represents the low matching degree for influenceing development stimulation practical operation situation and design in city；
The low species for influenceing development stimulation includes biology and is detained grid, permeable pavement, concave herbaceous field；
Low influence development stimulation is evaluated using " excellent, more excellent, good, poor, poor " five grades are qualitative, it is " excellent " to assign 100
Point, " more excellent " to assign 80 points, " good " to assign 60 points, " poor " to assign 40 points, " poor " to assign 20 points, city is low to influence exploitation design robustness
Index score value S6It is calculated as follows：
In formula (9), AkDevelopment stimulation floor space, A are influenceed for individual event is lowlFor the low influence total floor space of development stimulation, S6kFor list
The low influence development stimulation design robustness index score value of item, s are low influence development stimulation species number；
A7：Operation stability index, which is assigned, divides
The operation stability represents the low global reliability for influenceing development stimulation, using " excellent, more excellent, good, poor, poor " five
Individual grade is qualitative to be evaluated low influence development stimulation, and " excellent " to assign 100 points, " more excellent " 80 points of tax is " good " to assign 60 points, " compared with
Difference " assigns 40 points, and " poor " to assign 20 points, city is low to influence developing operation stability indicator score value S7It is calculated as follows：
In formula (10), AkDevelopment stimulation floor space, A are influenceed for individual event is lowlFor the low influence total floor space of development stimulation, S7kFor
Individual event is low to influence development stimulation operation stability index score value, and s is low influence development stimulation species number；
A8：Rainwater Resources are assigned using index divides
As Rainwater Resources utilization rate ε >=15%, Rainwater Resources assign 100 points using index；Work as ε<When 15%, pressAssign and divide, Rainwater Resources utilization rate ε is calculated as follows：
In formula (11), Q4For using rainwater resource amount, γ is season reduction coefficient every year,For comprehensive runoff coefficient, H3To be more
Average annual rainfall, A are survey region catchment area, H3In terms of m；
In formula (12), ε is Rainwater Resources utilization rate, Q3For average annual rainwater-collecting and the rainwater total amount that utilizes, Q3And Q4With m3Meter；
The season reduction coefficient γ is to eliminate the influence that non-rainy season rainfall is difficult by；
A9：Ecoscape index, which is assigned, divides
The ecoscape represent it is low influence overall space structure, interaction, coordination function that development stimulation formed and
Dynamic change, using " high benefit, more efficient be beneficial, medium benefit, compared with poor benefit, poor benefit, without benefit " six grades are qualitative right
Low influence development stimulation is evaluated, and " high benefit " assigns 100 points, and " more efficient benefit " assigns 80 points, and " medium benefit " assigns 60 points, " compared with
Poor benefit " assigns 40 points, and " poor benefit " assigns 20 points, and " no benefit " assigns 0 point, and city is low to influence exploitation ecoscape index score value S9Meter
Calculate as follows：
In formula (13), AkDevelopment stimulation floor space, A are influenceed for individual event is lowlFor the low influence total floor space of development stimulation, S9kFor
Individual event is low to influence development stimulation ecoscape index assignment, and s is low influence development stimulation species number.
6. a kind of city based on numerical simulation according to claim 5 is low to influence exploitation Optimal Configuration Method, its feature
It is, the significance analysis in the A3：
Assuming that have M low influence development plans, and it is low to influence have p group water quality parameters in the forward and backward water quality data of exploitation, N fields allusion quotation is set
Type rainfall, i.e., different precipitation guaranteed rates pr, i.e., single low influence development plan has N group samples, with single low influence exploitation side
In case exemplified by single water quality parameter, using Wilcoxon signed rank test methods, it is low on city influence to develop forward and backward single water quality it
Between difference carry out significance analysis, step is as follows：
B1：Calculate paired data difference
di=| x2,i-x1,i|, i=1,2 ..., N (14)
In formula (14), diInfluence to develop forward and backward water quality data difference to be low, if di=0 is given up, x2,iFor low water after influenceing exploitation
Matter statistical average, x1,iFor low water quality data average value before influenceing exploitation；
In formula (15), sgn (x2,i-x1,i) it is sign function, to judge (x2,i-x1,i) sign；
B2：The order of calculating difference
By diArranged by ascending order, obtain corresponding order Ri；
B3：Calculate test statistics
In formula (16), W+For (x2,i-x1,i) ＞ 0 | x2,i-x1,i| corresponding RiSum, be positive number, NrTo cast out diSample after=0
This amount；In formula (17), W-For (x2,i-x1,i) ＜ 0 | x2,i-x1,i| corresponding-RiSum, be negative；
B4：Establish hypothesis testing
In formula (18), MdFor the low Overall median number for influenceing to develop forward and backward water quality data difference, H0Think that low influence exploitation is forward and backward
The positive and negative intensity of variation of water quality data is substantially suitable, and distribution gap is smaller, H1Water quality data is in the presence of aobvious before and after thinking low influence exploitation
Write difference；
B5：Conspicuousness judges
Take test statistics W=min (W+,|W-|), construct the Z statistics of Normal Distribution：
Using statistical software or look into Wilcoxon signed rank test distribution tables and obtain in H0Probability p value under assuming that, give conspicuousness
Horizontal α, if p≤α, refuse null hypothesis H0, it is believed that before and after low influence exploitation there is significant difference in water quality data, if p>α, then connect
By null hypothesis H0, it is believed that water quality data is without significant difference before and after low influence exploitation.
7. a kind of city based on numerical simulation according to claim 5 is low to influence exploitation Optimal Configuration Method, its feature
It is, the water quality classification in the A3 differentiates：
With《Water environment quality standard (GB3838-2002)》Water quality classification is used as with reference to rank, is associated using improved grey model
Analytic approach, the forward and backward water quality classification of exploitation that influences low on city differentiate that step is as follows：
C1：Variable nondimensionalization
Assuming that have M low influence development plans, and it is low to influence have p group water quality parameters in the forward and backward water quality data of exploitation, N fields allusion quotation is set
Type rainfall, i.e., different precipitation guaranteed rates pr, i.e., single low influence development plan has N group samples, with single low influence exploitation side
Exemplified by case, matrix [x' is formedjk]N×p, the low influence obtained on mathematical modeling using Maximum Approach develops forward and backward water quality data
Nondimensionalization processing is carried out, obtains matrix [xjk]N×p：
In formula (21), x 'jkFor original water quality data, xjkFor water quality data after nondimensionalization, maxx'kFor under same water quality parameter
V class water quality classification maximum；
C2：It is determined that analysis ordered series of numbers
By taking single game typical case's rainfall as an example, it is determined that analysis ordered series of numbers：
X0={ x0(k) | k=1,2 ..., p } j=0 (22)
Xj={ xj(k) | k=1,2 ..., p } j=1,2 ..., m (23)
In formula (22), X0For reference sequence (female ordered series of numbers), influence to develop forward and backward water quality as reference sequence using low；
In formula (23), XjTo compare ordered series of numbers (subnumber row), it is using water quality assessment standard of all categories as ordered series of numbers, m is compared《Earth's surface water ring
Border quality standard (GB3838-2002)》Water quality classification number (I class, II class, III class, IV class and V class, i.e. m=5)；
C3：Calculate correlation coefficient
In formula (24), Δj(k) it is k-th of water quality assessment index X0With XjAbsolute difference, aj(k) it is water quality data xj(k) upper
Limit, bj(k) it is water quality data xj(k) lower limit；
In formula (25), ζj(k) it is incidence coefficient, P is resolution ratio, and interval is P=(0,1), and P is smaller, and resolving power is bigger；
C4：Calculating correlation
Low influence is calculated using mean value method and develops water quality data and water quality assessment standard of all categories under forward and backward single game typical case rainfall
The degree of association：
In formula (26), κjInfluence to develop water quality data and the pass of water quality assessment standard of all categories under forward and backward single game typical case rainfall to be low
Connection degree；
C5：Degree of association size sorts
The degree of association directly reflects good and bad relation of each comparative sequences for reference sequences, and degree of association descending is arranged, obtains ash
Inteerelated order, so that it is determined that low influence to develop forward and backward water quality classification.
8. a kind of city based on numerical simulation according to claim 5 is low to influence exploitation Optimal Configuration Method, its feature
It is, the pollutant sources analysis in the A3,
Using PCA, water quality parameter carries out dimension-reduction treatment after influence exploitation low on city, and step is as follows：
D1：Variable nondimensionalization
Assuming that have M low influence development plans, and it is low to influence have p water quality parameter in water quality data after developing, N fields typical case's drop is set
Rain, i.e., different precipitation guaranteed rates pr, i.e., single low influence development plan has a N group samples, and the M low development plans that influence have R=M
× N group samples, form matrix [x 'ij]R×p, after the low influence exploitation obtained using Z-score Standardization Acts on mathematical modeling
Water quality data carries out nondimensionalization processing, obtains normalized matrix [xij]R×p：
In formula (27),For jth row water quality data average value；
In formula (28), sjFor jth row water quality data standard deviation；
In formula (29), xijNondimensionalization water quality data after being standardized for water quality data Z-score；
D2：Calculate covariance matrix
Covariance matrix (correlation matrix) J=[sij]p×p：
In formula (30), sijFor water quality data XiAnd XjCoefficient correlation,For the i-th row water quality data average value；
D3：Calculate characteristic value and characteristic vector
Characteristic equation is solved with Jacobi methods | λ I-J |=0, obtain eigenvalue λi(i=1,2 ..., p, λ is arranged in descending order1≥λ2
≥…≥λp) and characteristic value corresponding to orthogonalization unit character vector ei(i=1,2 ..., p), eijRepresent vectorial eiJ-th point
Amount；
D4：Calculate principal component contributor rate and contribution rate of accumulative total
In formula (31), ρiFor principal component contributor rate；
In formula (32), G (g) is contribution rate of accumulative total, as G (g) >=85%, it is believed that can reflect the information of raw water qualitative change amount, g is most
The principal component number selected eventually；
D5：Calculate principal component expression formula
Fi=ei1X1+ei2X2+…+eipXp, i=1,2 ..., g (33)
In formula (33), FiFor the principal component of water correction evaluation index, X is the water quality data after nondimensionalization；
In formula (34), F is water pollution comprehensive score, is the quantitative description of water pollution degree.
9. a kind of city based on numerical simulation according to claim 1 is low to influence exploitation Optimal Configuration Method, its feature
It is, it is as follows with reference to Delphi method and analytic hierarchy process (AHP), agriculture products weight, step in the step (4)：
E1：Target layers are built
Hi=(environment, economic, society)
Hij=(exemplified by two-level index corresponding to " environment " first class index)=(footpath flow control, water correction)
Hijk=(exemplified by " footpath flow control " three-level index corresponding to two-level index)=(runoff volume, runoff peak value) (35)
In formula (35), HiFor first class index, HijFor corresponding to HiTwo-level index, HijkFor corresponding to HijThree-level index；
E2：Index judgment matrix is built
Expert understands the low influence exploitation optimizing evaluation system in city, and anonymous to target layers construction, relative to last layer time, some refers to
For mark, in this level between index significance level judgment matrix A'；
Assuming that an indicator layer Elements C is criterion above, the next indicator layer element dominated is u1,u2,…un(n is element
Number), if u1,u2,…unThe importance of Elements C can be quantified, weight can be determined directly, conversely, using comparative approach two-by-two, be sentenced
Break for Elements C, element uiAnd ujSignificance level, importance degree is assigned according to 1~9 proportion quotiety and divided, obtains judgment matrix A'
=[a 'ij]n×n, wherein a 'ijFor element uiAnd ujRelative to the significance level of Elements C；
E3：Judgment matrix approach is assessed
In formula (36), CI is judgment matrix average value, and n is matrix exponent number；
In formula (37), CR is random Consistency Ratio, and RI is same order mean random number；WhenWhen, judgment matrix tool
There is satisfied uniformity, it is on the contrary, it is believed that expert, which assesses, has larger error, readjusts judgment matrix and is allowed to meet that uniformity will
Ask, obtain A=[aij]n×n；
E4：Relative weighting calculates
Calculate judgment matrix A λmaxAnd its corresponding unit vector, then it is index relative weighting；
Assuming that AiFor i-th of three-level index in low influence development effectiveness System of Comprehensive Evaluation, X, Y, Z is respectively the index institute
Corresponding one-level, two level, three-level relative weighting, then the index weights are ωi=XYZ；Other three-levels can be calculated by that analogy to refer to
Mark weight.
10. a kind of city based on numerical simulation according to claim 1 is low to influence exploitation Optimal Configuration Method, its feature
It is, city is low in the step (5) influences exploitation optimization scheme screening：
According to the index weights determined in the index score and step (4) determined in step (3), calculate the low influence in M city and open
Scheme points S is sent out, score the maximum, which is that city is low, influences exploitation optimization scheme；
In formula (38), S (j), which is that city is low, influences development plan score, SiExploitation indices score, ω are influenceed for city is lowiFor
City is low to influence exploitation three-level index weights.
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