CN107274018A - A kind of sponge city LID measure best of breed optimization evaluation methods based on TFN AHP methods - Google Patents
A kind of sponge city LID measure best of breed optimization evaluation methods based on TFN AHP methods Download PDFInfo
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Abstract
A kind of sponge city LID measure best of breed optimization evaluation methods based on TFN AHP methods, belong to sponge city measure study on assessing method technical field.This method initially sets up individual event sponge city LID measures ratio and selects system, the multi-layer target system of sponge city LID measure various combinations is set up using analytic hierarchy process (AHP), by using Triangular Fuzzy Number Judgement Matricies, determine each layer index weights, weight of each index for general objective is determined again, it is final to determine comprehensive weight, so as to obtain sponge city LID combination measure best of breed optimizations, this method is complicated and have obvious advantage when can not carry out quantitative analysis to system in system architecture, the various combination measure appraisal procedure can also be used for other assessments in addition to sponge city LID combined measures optimize simultaneously, with preferable versatility.
Description
Technical field
The invention belongs to sponge city measure study on assessing method technical field, more particularly to one kind is based on fuzzy hierarchy point
The sponge city LID measure best of breed optimization evaluation methods of analysis.
Background technology
Because the country starts late in sponge urban construction at present, and technology is relatively weak；But for sponge city
Build low influence development technique measure (LID) various combination research it is relatively fewer, and at home planning and designing when,
Effective appraisal procedure is not unified using different technologies combination measure, causes the use to technical measures to lack scientific, effective
Property, reasonability and economy.It is excellent by the best of breed for carrying out various combination optimization to the different single technology measures in sponge city
Change assessment to refer to using qualitative and be quantitatively combined means, analysis, calculate, assess sponge city LID measures various combination and optimize
The selection that best of breed optimization is reached in evaluation process is target, premised on the landing of sponge urban construction scheme is implemented, power
Ask and provide a set of science for sponge Process of Urban Development moderate rain water management and utilizing works construction, it is reasonable, possess complete operability
Best of breed optimization evaluation method.
The content of the invention
For lack in terms of current sponge city LID Measure choices assessment effectively, the appraisal procedure of system the problem of, this hair
It is bright to propose a kind of sponge city LID measure best of breed optimization evaluation methods for being based on Triangular Fuzzy Number (TFN-AHP).The party
Method initially sets up individual event sponge city LID measures ratio and selects system, and the different groups of sponge city LID measures are set up using analytic hierarchy process (AHP)
The multi-layer target system of conjunction, by using Triangular Fuzzy Number Judgement Matricies, determines each layer index weights, then determine each finger
The weight for general objective is marked, comprehensive weight is finally determined, so that sponge city LID combination measure best of breed optimizations are obtained,
This method is complicated and have obvious advantage when can not carry out quantitative analysis to system in system architecture, while the various combination
Measure appraisal procedure can also be used for other assessments in addition to sponge city LID combined measures optimize, with preferable versatility.
The present invention is to realize completion by following technical solution, and appraisal procedure step specific as follows is realized：
The first step：Set up individual event sponge city LID measures ratio and select system.
By using the LID measures of individual event sponge city in technical (A1), economy (A2) and social benefit (A3) index
Aspect deployment analysis.Technical index is mainly divided including run-off reduction rate (B1), rain peak quantity curtailment rate (B2), runoff pollution
Three kinds of indexs of reduction rate (B3), technical index represents that run-off reduction rate index is represented with B1 with A1, rain peak quantity curtailment rate
Index B2, runoff pollution reduction rate index represents with B3, i.e. A1={ B1, B2, B3 }；Economic index mainly include capital construction into
This (B4), operation and two kinds of indexs of maintenance cost (B5), economic indicator represent that the capital construction indicator of costs is represented with B4 with A2, run
Represented with maintenance cost with B5, i.e. A2={ B4, B5 }；Social benefit index mainly includes landscape effect (B6), ecological functions
(B7) two kinds of indexs, social benefit index represents that landscape effect is represented with B6 with A3, and ecological functions are represented with B7, i.e. A3=
{ B6, B7 }；The LID measures of sponge city mainly include Green Roof (O1), permeable pavement (O2), concave herbaceous field (O3), rainwater
Storage pond (O4), sponge city LID measures represent that Ox represents each individual event LID measures, wherein x=1,2,3,4, such as green room with T
Very useful O1 represents that permeable pavement is represented with O2, and concave herbaceous field is represented with O3, and storm detention tank is represented with O4, i.e. Z=O1,
O2, O3, O4 }；The LID measures of each single item sponge city are to that should have B1-B7 index；
By using each index of individual event sponge city LID measures, set up single measure ratio and select frame diagram Fig. 1 and individual event
LID measures index ratio in sponge city selects table table 1.
The individual event sponge city LID measures index ratio of table 1 selects table
Second step：The LID measures of individual event sponge city are subjected to various combination, different measure assembled scheme is formed.Just at present
Four kinds of measures form 11 kinds of various combination schemes, and wherein Green Roof+permeable pavement is represented with scheme S1, Green Roof+recessed
Formula greenery patches represents that Green Roof+storm detention tank is represented with scheme S3 with scheme S2, permeable pavement+concave herbaceous field scheme S4
Represent, permeable tile work+storm detention tank represents that concave herbaceous field+storm detention tank is represented with scheme S6 with scheme S5, green room
Top+permeable tile work+concave herbaceous field represents that Green Roof+permeable tile work+storm detention tank is represented with scheme S8 with scheme S7,
Permeable tile work+concave herbaceous field+storm detention tank represents with scheme S9, Green Roof+concave herbaceous field+storm detention tank side
Case S10 represents that Green Roof+permeable pavement+concave herbaceous field+storm detention tank is represented with scheme S11.
3rd step：Build sponge city LID combined measure schemes Recurison order hierarchy analysis system.The rank step analysis system is
Four layers, including destination layer, rule layer, indicator layer and solution layer；I.e. destination layer is estimated default or to be obtained index
Index request in layer；Rule layer is technical (A1), economy (A2) and social effect (A3)；Indicator layer is that run-off is cut down
Rate (B1), rain peak quantity curtailment rate (B2), runoff pollution reduction rate (B3), capital construction cost (B4), operation and maintenance cost (B5),
Landscape effect (B6) and ecological functions (B7)；Solution layer is scheme S1：Green Roof+permeable pavement, scheme S2：Green Roof
+ concave herbaceous field, scheme S3：Green Roof+storm detention tank, scheme S4：Permeable pavement+concave herbaceous field, scheme S5：It is permeable
Tile work+storm detention tank, scheme S6：Concave herbaceous field+storm detention tank, scheme S7：Green Roof+permeable tile work+up concave type
Greenery patches, scheme S8：Green Roof+permeable tile work+storm detention tank, scheme S9：Permeable tile work+concave herbaceous field+rainwater storage
Pond, scheme S10：Green Roof+concave herbaceous field+storm detention tank, scheme S11：Green Roof+permeable pavement+up concave type is green
Ground+storm detention tank；Each single item scheme is to that should have B1-B7 index；Recurison order hierarchy analysis system is shown in Fig. 2.
4th step：Fuzzy judgment matrix is constructed using Triangular Fuzzy Number.
(1) fuzzy set is constructed.If domain T, T are in any mapping of closed interval [0,1]Then
A T fuzzy subset is determined, abbreviation fuzzy set is denoted asReferred to as fuzzy setMembership function,It is member
Plain t is under the jurisdiction ofDegree, referred to as degree of membership.
(2) Triangular Fuzzy Number concept.In order to represent that a certain feature x is under the jurisdiction of set M degree, typically conventional triangle letter
Number expresses fuzzy membership functions, for any Triangular Fuzzy Number characteristic value between 3 points of the x=[l, m, u].If
It is a triangle ambiguity function, then its membership function is
Wherein, l≤m≤u, and x ∈ R | l ＜ x ＜ u },
(3) fuzzy judgment matrix is constructed.System and the LID measures of sponge city are selected using individual event sponge city LID measures ratio
Assembled scheme Recurison order hierarchy analyzes system, by expert with the index Bt of indicator layer (t=1,2 ..., be 7) in criterion other side's pattern layer
All schemes carry out two-by-two important ratio compared with and constructing fuzzy judgment matrix using Triangular Fuzzy Number.
Order has n (n=1,2 ..11) individual evaluation index, then the judgment matrix constructed is B=(bij)n×n, wherein bij=
[lij,mij,uij] it is with mijFor the closed interval of median, andbijUnder the conditions of representing a certain criterion
Index BiTo BjThe numerical value of relative importance embodies, and compares and selects table according to for the individual event sponge city LID measures index ratio of table 1, leads to
Normal bijUsing 1-9 scaling laws, its implication is as shown in table 2.
The B of table 2iAnd BjCorresponding index i and j 1-9 scaling laws and its implication in comparing
Judge comparison, b are carried out provided with H expertsijIt is then comprehensive Triangular Fuzzy Number, wherein
(h=1,2 ..., H), and bij h=[lij h,mij h,uij h] it is the Triangular Fuzzy Number that h-th of expert provides；
5th step：Utilize judgment matrix construction fuzzy evaluation factor matrix E.Computational methods are as follows：
Wherein,For standard profit rate, its value reflects the fog-level that expert judging compares；sijIt is bigger,
Fog-level is bigger, conversely, fog-level is smaller.
6th step：Calculate adjustment judgment matrix Q
In formula, matrix M is the fuzzy median m of all triangles in judgment matrixijThe matrix of composition；
7th step：Will adjustment judgment matrix Q=(qij)n×nThe judgment matrix that diagonal is 1 is converted into by row, is designated as judging
Matrix P, then P=(pij)n×n, and meetAs i >=j,As i ＜ j,
8th step：Line translation is entered to matrix P using consistent matrix analytic approach, consistent matrix R=(r are obtainedij)n×n, R satisfactions
Condition for consistence rij=rik·rkj(k=1,2 ... 11), andMeet simultaneously
9th step：Calculate in indicator layer each index respectively in solution layer scheme weights omegaα—Si.Wherein
Tenth step：Calculate comprehensive weight ω of each index for general objective；First, it is determined that suitable for the influence of survey region
Index is the index request in estimated default or to be obtained indicator layer, calculates the corresponding difference of index for obtaining indicator layer
Weights omegaα(α=Bi, Bi=B1, B2, B3, B4, B5, B6, B7)；Weighed according to index weights are tried to achieve with the scheme under indicator conditions
Comprehensive weight calculating is carried out again, then
Indicator layer weight and each scheme weighted value calculate obtaining comprehensive weight value, and are ranked up, is selected optimal
Combinatorial Optimization measure.
11st step：It is ranked up according to the size of synthetic weights weight values, the scheme set V={ v arranged from big to small1,
v2,...,v11}.The wherein maximum scheme of synthetic weights weight values is best of breed Optimized Measures.
By original measure scheme sn(11) n=1,2 ..., bring into simplation verification carried out in PCSWMM models respectively, will be
Different index Bt (t=1,2 ..., 7) under the conditions of analog result sort, analog result sequence is with utilizing triangle Fuzzy Level Analytic Approach
Method (TFN-AHP) obtains scheme weights omega under the conditions of calculating using indicator layer as criterioniSequence matches, then illustrates that this method has
Effect, it is feasible so that according to comprehensive weight set V={ v1,v2,...,v11Obtain final optimal sponge city LID measures
Combinatorial Optimization scheme.
Brief description of the drawings
Fig. 1 individual event sponges city LID measures ratio selects frame diagram；
Fig. 2 sponges city LID combined measure schemes Recurison order hierarchy analysis system；
Fig. 3 PCSWMM model system outflow change curves；
Percentage block diagram is cut down at the rainy peak of different schemes measure in Fig. 4 PCSWMM models；
Embodiment
With reference to embodiment, the invention will be further described, but the present invention is not limited to following examples.
Embodiment 1
By being selected as with sponge city LID stimulation optimization assembled schemes in the sponge urban construction of Shenzhen somewhere
The case study on implementation of the present invention, the implementation case is implemented lower premised on the technology of the present invention, and gives detailed reality
Mode and specific operating process are applied, but the application of the present invention is not limited to following case study on implementation.The implementation case is sea
Continuous city LID measures best of breed optimized evaluation process citing.The implementation case detailed process includes following steps：
The first step：Set up individual event sponge city LID measures ratio and select system.
By using the LID measures of individual event sponge city in technical (A1), economy (A2) and social benefit (A3) index
Aspect deployment analysis.Technical index is mainly divided including run-off reduction rate (B1), rain peak quantity curtailment rate (B2), runoff pollution
Three kinds of indexs of reduction rate (B3), technical index represents that run-off reduction rate index is represented with B1 with A1, rain peak quantity curtailment rate
Index B2, runoff pollution reduction rate index represents with B3, i.e. A1={ B1, B2, B3 }；Economic index mainly include capital construction into
This (B4), operation and two kinds of indexs of maintenance cost (B5), economic indicator represent that the capital construction indicator of costs is represented with B4 with A2, run
Represented with maintenance cost with B5, i.e. A2={ B4, B5 }；Social benefit index mainly includes landscape effect (B6), ecological functions
(B7) two kinds of indexs, social benefit index represents that landscape effect is represented with B6 with A3, and ecological functions are represented with B7, i.e. A3=
{ B6, B7 }；The LID measures of sponge city mainly include Green Roof (O1), permeable pavement (O2), concave herbaceous field (O3), rainwater
Storage pond (O4), sponge city LID measures represent that Ox represents each individual event LID measures, wherein x=1,2,3,4, such as green room with T
Very useful O1 represents that permeable pavement is represented with O2, and concave herbaceous field is represented with O3, and storm detention tank is represented with O4, i.e. T=O1,
O2, O3, O4 }.
By using each index of individual event sponge city LID measures, set up single measure ratio and select frame diagram Fig. 1 and individual event
LID measures index ratio in sponge city selects table table 1.
The individual event sponge city LID measures index ratio of table 1 selects table
Second step：The LID measures of individual event sponge city are subjected to various combination, different measure assembled scheme is formed.Just at present
Four kinds of measures form 11 kinds of various combination schemes, and wherein Green Roof+permeable pavement is represented with scheme S1, Green Roof+recessed
Formula greenery patches represents that Green Roof+storm detention tank is represented with scheme S3 with scheme S2, permeable pavement+concave herbaceous field scheme S4
Represent, permeable tile work+storm detention tank represents that concave herbaceous field+storm detention tank is represented with scheme S6 with scheme S5, green room
Top+permeable tile work+concave herbaceous field represents that Green Roof+permeable tile work+storm detention tank is represented with scheme S8 with scheme S7,
Permeable tile work+concave herbaceous field+storm detention tank represents with scheme S9, Green Roof+concave herbaceous field+storm detention tank side
Case S10 represents that Green Roof+permeable pavement+concave herbaceous field+storm detention tank is represented with scheme S11.
3rd step：Build sponge city LID combined measure schemes Recurison order hierarchy analysis system.The rank step analysis system is
Four layers, including destination layer, rule layer, indicator layer and solution layer；I.e. destination layer is estimated index request；Rule layer is technology
Property (A1), economy (A2) and social effect (A3)；Indicator layer be run-off reduction rate (B1), rain peak quantity curtailment rate (B2),
Runoff pollution reduction rate (B3), capital construction cost (B4), operation and maintenance cost (B5), landscape effect (B6) and ecological functions
(B7)；Solution layer is scheme S1：Green Roof+permeable pavement, scheme S2：Green Roof+concave herbaceous field, scheme S3：Green
Roof+storm detention tank, scheme S4：Permeable pavement+concave herbaceous field, scheme S5：Permeable tile work+storm detention tank, scheme S6：
Concave herbaceous field+storm detention tank, scheme S7：Green Roof+permeable tile work+concave herbaceous field, scheme S8：Green Roof+thoroughly
Water tile work+storm detention tank, scheme S9：Permeable tile work+concave herbaceous field+storm detention tank, scheme S10：Green Roof+recessed
Formula greenery patches+storm detention tank, scheme S11：Green Roof+permeable pavement+concave herbaceous field+storm detention tank；Recurison order hierarchy point
Analysis system system is shown in Fig. 2.
4th step：Fuzzy judgment matrix is constructed using Triangular Fuzzy Number.
(1) fuzzy set is constructed.If domain T, T are in any mapping of closed interval [0,1]Then
A T fuzzy subset is determined, abbreviation fuzzy set is denoted asReferred to as fuzzy setMembership function,It is member
Plain t is under the jurisdiction ofDegree, referred to as degree of membership.
(2) Triangular Fuzzy Number concept.In order to represent that a certain feature x is under the jurisdiction of set M degree, typically conventional triangle letter
Number expresses fuzzy membership functions, for any Triangular Fuzzy Number characteristic value between 3 points of the x=[l, m, u].If
It is a triangle ambiguity function, then its membership function is
Wherein, l≤m≤u, and u-m=m-l=1, and x ∈ R | l ＜ x ＜ u },
(3) fuzzy judgment matrix is constructed.System and the LID measures of sponge city are selected using individual event sponge city LID measures ratio
Assembled scheme Recurison order hierarchy analyzes system, by expert with the index Bt of indicator layer (t=1,2 ..., be 7) in criterion other side's pattern layer
All schemes carry out two-by-two important ratio compared with and constructing fuzzy judgment matrix using Triangular Fuzzy Number.Order has n (, 1,2 ...) individual
Evaluation index, the then judgment matrix constructed is B=(bij)n×n, wherein bij=[lij,mij,uij] it is with mijFor the closed zone of median
Between, andbijRepresent B under the conditions of a certain criterioniTo BjThe numerical value of relative importance embodies, generally
bijUsing 1-9 scaling laws, i and j represents ith row and jth column respectively；Its implication is as shown in table 2.
The B of table 2iAnd BjCorresponding index i and j 1-9 scaling laws and its implication in comparing
Judge comparison, b are carried out provided with H expertsijIt is then comprehensive Triangular Fuzzy Number, wherein
(h=1,2 ..., H), and bij h=[lij h,mij h,uij h] it is the Triangular Fuzzy Number that h-th of expert provides.
Fuzzy judgment matrix B=(b with " B2 rain peak quantity curtailment rate " to be constructed under the conditions of criterionij)11×11, present case
Write matrix form as form, the meaning of expression is identical；Wherein using diagonal of a matrix as line of demarcation, when i is more important than j
When, mijRound numbers, and in matrix lower half angular position, otherwise when j is more important than i, mijRound numbers, and in matrix upper half triangle
Position, in other words：For B2 rain peak quantity curtailment rate, different sponge city LID arranges in two schemes compared
Apply which relatively important or influence on corresponding B2 rain peak quantity curtailment rate technical indicator better than larger or effect, then in B
Quantity curtailment rate technical indicator best sponge city in B2 rain peak in the crosspoint selection table 1 of two schemes compared in matrix
Technical scheme where LID measures, the then table 2 corresponding to the best or optimal B2 rain peak quantity curtailment rate technical indicator of effect
In relative weighting be used as bijIn mij；Such as it is compared using S2 as i-th with S5 as jth, in Green Roof, up concave type
Greenery patches, permeable tile work, storm detention tank the B2 rain peak quantity curtailment rate technical indicator in table 1 are best for storm detention tank, rain
Water storage pond in S5 technical schemes, it is corresponding be ☆ ☆ ☆, ☆ ☆ ☆ in table 2 it is generally acknowledged that for 4, between somewhat important and
3 between important, the then b that S2 and S5 is comparedijIn mij4 are taken, because S5 technical schemes are important (i.e. when j is more important than i),
So corresponding in matrix upper half angular position.
Table 3 " B2 rain peak quantity curtailment rate " is the fuzzy judgment matrix B=(b that construct under the conditions of criterionij)11×11
5th step：Utilize judgment matrix construction fuzzy evaluation factor matrix E.Computational methods are as follows：
In formula,For standard profit rate, its value reflects the fog-level that expert judging compares.sijIt is bigger,
Fog-level is bigger, conversely, fog-level is smaller.
Fuzzy evaluation factor matrix E is obtained according to judgment matrix B.There can be the decimal error for thinking to calculate.
The fuzzy evaluation factor matrix E of table 4
| 1.0000 | 0.3333 | 0.7917 | 0.5000 | 0.6250 | 0.7333 | 0.6250 | 0.8286 | 0.7917 | 0.8542 | 0.8542 |
| 0.5000 | 1.0000 | 0.6250 | 0.6667 | 0.7333 | 0.6250 | 0.3333 | 0.7917 | 0.7333 | 0.7917 | 0.8730 |
| 0.8000 | 0.6667 | 1.0000 | 0.8000 | 0.5000 | 0.3333 | 0.6667 | 0.3333 | 0.6250 | 0.3333 | 0.7333 |
| 0.3333 | 0.6250 | 0.7917 | 1.0000 | 0.7917 | 0.7917 | 0.6250 | 0.8286 | 0.7917 | 0.8286 | 0.8542 |
| 0.6667 | 0.7500 | 0.3333 | 0.8000 | 1.0000 | 0.3333 | 0.6667 | 0.6250 | 0.6250 | 0.6250 | 0.7333 |
| 0.7500 | 0.6667 | 0.5000 | 0.8000 | 0.5000 | 1.0000 | 0.6667 | 0.7917 | 0.3333 | 0.6250 | 0.7333 |
| 0.6667 | 0.5000 | 0.6250 | 0.6667 | 0.6250 | 0.6250 | 1.0000 | 0.7333 | 0.7333 | 0.7333 | 0.7917 |
| 0.8333 | 0.8000 | 0.5000 | 0.8333 | 0.6667 | 0.8000 | 0.7500 | 1.0000 | 0.6667 | 0.3333 | 0.3333 |
| 0.8000 | 0.7500 | 0.6667 | 0.8000 | 0.6667 | 0.5000 | 0.7500 | 0.6250 | 1.0000 | 0.3333 | 0.6250 |
| 0.8571 | 0.8000 | 0.5000 | 0.8333 | 0.6667 | 0.6667 | 0.7500 | 0.5000 | 0.5000 | 1.0000 | 0.3333 |
| 0.8571 | 0.8750 | 0.7500 | 0.8571 | 0.7500 | 0.7500 | 0.8000 | 0.5000 | 0.6667 | 0.5000 | 1.0000 |
6th step：Calculate adjustment judgment matrix Q.
In formula, matrix M is the fuzzy median m of all triangles in judgment matrixijThe matrix of composition.Can have and think what is calculated
Decimal error.
The adjustment judgment matrix of table 5 Q
| 3.2524 | 3.3226 | 3.7272 | 4.2226 | 3.7885 | 3.7010 | 3.3937 | 4.0334 | 3.8806 | 3.9193 | 4.2084 |
| 5.1619 | 5.0405 | 5.9396 | 6.3738 | 5.7813 | 5.8190 | 5.1569 | 6.3512 | 6.1007 | 6.2406 | 6.6265 |
| 14.0012 | 13.0604 | 14.4931 | 15.6143 | 14.9847 | 13.9625 | 13.7833 | 15.9232 | 15.4417 | 15.7873 | 16.8940 |
| 2.2298 | 2.2500 | 2.3780 | 2.7347 | 2.4196 | 2.3599 | 2.2962 | 2.5476 | 2.4827 | 2.4410 | 2.6551 |
| 11.1528 | 12.0438 | 12.5347 | 13.8032 | 12.9958 | 12.3764 | 11.6167 | 14.0161 | 13.4597 | 13.8567 | 14.8476 |
| 13.9167 | 13.4788 | 14.8292 | 16.0587 | 14.9097 | 13.7947 | 13.9333 | 15.5446 | 15.3250 | 15.3679 | 17.0885 |
| 7.1996 | 6.8319 | 7.8028 | 8.0881 | 7.6583 | 7.6472 | 6.8058 | 8.5027 | 8.1528 | 8.4093 | 8.9189 |
| 24.6071 | 23.5542 | 23.7500 | 27.0786 | 24.5417 | 24.4500 | 23.6083 | 26.7095 | 25.1417 | 24.7381 | 27.9901 |
| 19.0254 | 18.0833 | 19.2500 | 21.0135 | 19.4889 | 17.9750 | 18.5583 | 20.2190 | 19.8944 | 19.7498 | 22.3032 |
| 24.5690 | 23.0042 | 23.6250 | 26.4286 | 24.5000 | 23.8167 | 23.2750 | 26.5798 | 25.1833 | 24.8423 | 27.2526 |
| 32.1714 | 31.8667 | 31.2917 | 35.3571 | 32.3250 | 30.6500 | 30.4667 | 33.9750 | 32.9500 | 32.2792 | 35.9091 |
7th step：Will adjustment judgment matrix Q=(qij)n×nThe judgment matrix that diagonal is 1 is converted into by row, is designated as judging
Matrix P, then P=(pij)n×n, and meetConversion regime is using as follows：As i >=j,As i ＜ j,
Adjustment judgment matrix Q is converted into the judgment matrix P that diagonal is 1 by row.There can be the decimal error for thinking to calculate.
The judgment matrix P of table 6
| 1.0000 | 0.6592 | 0.2572 | 1.5441 | 0.2915 | 0.2683 | 0.4986 | 0.1510 | 0.1951 | 0.1578 | 0.1172 |
| 1.5871 | 1.0000 | 0.4098 | 2.3307 | 0.4449 | 0.4218 | 0.7577 | 0.2378 | 0.3067 | 0.2512 | 0.1845 |
| 4.3049 | 2.5911 | 1.0000 | 5.7097 | 1.1530 | 1.0122 | 2.0252 | 0.5962 | 0.7762 | 0.6355 | 0.4705 |
| 0.6856 | 0.4464 | 0.1641 | 1.0000 | 0.1862 | 0.1711 | 0.3374 | 0.0954 | 0.1248 | 0.0983 | 0.0739 |
| 3.4291 | 2.3894 | 0.8649 | 5.0475 | 1.0000 | 0.8972 | 1.7069 | 0.5248 | 0.6766 | 0.5578 | 0.4135 |
| 4.2789 | 2.6741 | 1.0232 | 5.8723 | 1.1473 | 1.0000 | 2.0473 | 0.5820 | 0.7703 | 0.6186 | 0.4759 |
| 2.2136 | 1.3554 | 0.5384 | 2.9576 | 0.5893 | 0.5544 | 1.0000 | 0.3183 | 0.4098 | 0.3385 | 0.2484 |
| 7.5658 | 4.6730 | 1.6387 | 9.9019 | 1.8884 | 1.7724 | 3.4688 | 1.0000 | 1.2638 | 0.9958 | 0.7795 |
| 5.8497 | 3.5876 | 1.3282 | 7.6841 | 1.4996 | 1.3030 | 2.7268 | 0.7570 | 1.0000 | 0.7950 | 0.6211 |
| 7.5541 | 4.5639 | 1.6301 | 9.6643 | 1.8852 | 1.7265 | 3.4199 | 0.9951 | 1.2658 | 1.0000 | 0.7589 |
| 9.8916 | 6.3221 | 2.1591 | 12.9292 | 2.4873 | 2.2219 | 4.4766 | 1.2720 | 1.6562 | 1.2994 | 1.0000 |
8th step：Line translation is entered to matrix P using consistent matrix analytic approach, consistent matrix R=(r are obtainedij)n×n, R satisfactions
Condition for consistence rij=rik·rkj(k=1,2 ... 11), and rii=1,Meet simultaneously
Line translation is entered to matrix P using consistent matrix analytic approach, consistent matrix R=(r are obtainedij)11×11.Can have and think
The decimal error of calculating.
Consistent matrix R=(the r of table 7ij)11×11
| 1.0980 | 0.6932 | 0.2589 | 1.5132 | 0.2935 | 0.2664 | 0.5184 | 0.1528 | 0.1979 | 0.1591 | 0.1201 |
| 1.7104 | 1.0798 | 0.4034 | 2.3571 | 0.4571 | 0.4150 | 0.8075 | 0.2380 | 0.3083 | 0.2478 | 0.1871 |
| 4.3472 | 2.7444 | 1.0252 | 5.9907 | 1.1618 | 1.0548 | 2.0523 | 0.6048 | 0.7835 | 0.6299 | 0.4756 |
| 0.7110 | 0.4489 | 0.1677 | 0.9798 | 0.1900 | 0.1725 | 0.3357 | 0.0989 | 0.1281 | 0.1030 | 0.0778 |
| 3.7808 | 2.3869 | 0.8916 | 5.2102 | 1.0104 | 0.9174 | 1.7849 | 0.5260 | 0.6814 | 0.5478 | 0.4136 |
| 4.3563 | 2.7502 | 1.0273 | 6.0033 | 1.1642 | 1.0570 | 2.0566 | 0.6061 | 0.7851 | 0.6312 | 0.4766 |
| 2.2790 | 1.4387 | 0.5374 | 3.1406 | 0.6091 | 0.5530 | 1.0759 | 0.3171 | 0.4107 | 0.3302 | 0.2493 |
| 7.3311 | 4.6282 | 1.7288 | 10.1027 | 1.9593 | 1.7788 | 3.4610 | 1.0200 | 1.3213 | 1.0622 | 0.8020 |
| 5.7204 | 3.6114 | 1.3490 | 7.8831 | 1.5288 | 1.3880 | 2.7006 | 0.7959 | 1.0310 | 0.8289 | 0.6258 |
| 7.2498 | 4.5769 | 1.7097 | 9.9907 | 1.9375 | 1.7591 | 3.4226 | 1.0087 | 1.3066 | 1.0505 | 0.7931 |
| 9.5387 | 6.0219 | 2.2494 | 13.1450 | 2.5493 | 2.3145 | 4.5033 | 1.3271 | 1.7191 | 1.3821 | 1.0435 |
9th step：Calculate in indicator layer each index respectively in solution layer scheme weights omegaα—Si.Wherein
Calculated under the conditions of with " B2 rain peak quantity curtailment rate " for criterion and obtain ωB2—Si, i.e. ωB2—SiIt is expressed as with " B2 rain
Peak quantity curtailment rate " is the weight of each scheme under the conditions of criterion, and then individual scheme weighted value is ranked up.
Table 8 obtains each scheme weighted value ω under the conditions of with " B2 rain peak quantity curtailment rate " for criterionB2—SiSequencing table
| B2 | S1 | S2 | S3 | S4 | S5 | S6 | S7 | S8 | S9 | S10 | S11 |
| Weight | 0.0228 | 0.0355 | 0.0903 | 0.0148 | 0.0786 | 0.0905 | 0.0474 | 0.1523 | 0.1189 | 0.1507 | 0.198 2 |
| Sequence | 10 | 9 | 6 | 11 | 7 | 5 | 8 | 2 | 4 | 3 | 1 |
It is being that criterion condition obtains weight sequencing situation it is known that scheme S11 (greens with " B2 rain peak quantity curtailment rate "
Roof+permeable pavement+concave herbaceous field+storm detention tank) it is best in terms of rain peak reduction effect, next to that scheme S8 (greens
Roof+permeable pavement+storm detention tank), what be ranked third is scheme S10 (Green Roof+concave herbaceous field+storm detention tank),
Comparatively the worst scheme S4 of rain peak quantity curtailment effect (permeable pavement+concave herbaceous field).
According to same method, then using each index as criterion under the conditions of obtain each weighted value, refer to table 9；In its table 9
Weighted value of certain scheme under the conditions of certain index is criterion is represented by ωIndex-schemeI.e.It is criterion bar for example in index B1
The weights omega shared by scheme S1 under partB1—S1=0.0130.
Each scheme weighted value under each indicator conditions of table 9
Tenth step：First, it is determined that the influence index suitable for survey region is estimated default or to be obtained index
Index in layer is destination layer.Influence index considers following factor when selecting：1. B1 run-offs reduction rate；2. B2 rain peak flow is cut
Lapse rate；3. B3 runoff pollutions reduction rate；4. B4 capital construction cost；5. B5 is run and maintenance cost；6. B6 landscape effects；7. B7 is ecological
Function.
Secondly survey region index assignment is carried out, the fuzzy judgment matrix Y=(y of different indexs are set upij)7×7, for example：
B1 run-offs reduction rate and B2 rain peak quantity curtailment rate are determined when entering row index assignment first, at the same also to consider B4 capital construction into
This, B5 operation expenses, next to that consider B3 runoff pollutions reduction rates, B6 landscape effects, and B7 ecological functions are carried out with
Assignment to a certain degree.That is the relative importance relation of influence index can be approximately represented as B1 >=B2 >=B4 ＞ B5 >=B3 >=B6 ＞
B7, and as basis for estimation, the fuzzy judgment matrix Y under the Different Effects index finally set up is shown in Table 10.
The fuzzy judgment matrix Y of the Different Effects index of table 10
Following each index weights ω is obtained by being calculated with the computational methods of scheme weightα(α=Bi, Bi=B1,
B2, B3, B4, B5, B6, B7), and be ranked up.It is shown in Table 11.
Each weighted value of indicator layer of table 11 ωαAnd its sequence
| B1 | B2 | B3 | B4 | B5 | B6 | B7 | |
| Weight | 0.2929 | 0.2089 | 0.1261 | 0.1678 | 0.1042 | 0.064 | 0.0361 |
| Sequence | 1 | 2 | 4 | 3 | 5 | 6 | 7 |
11st step：Calculate comprehensive weight ω of each index for general objective.By the different index bars in parameter layer
Different weights omegas are corresponded under part in solution layerα—Si, calculated according to same procedure and obtain the corresponding different weights of indicator layer
ωα.According to index weights and the scheme weight progress comprehensive weight calculating under indicator conditions are tried to achieve, then Indicator layer weight is counted with each scheme weighted value
The synthetic weights weight values obtained in following table are calculated, and are ranked up, best of breed Optimized Measures is selected, refers to table 12；For example
Scheme S1 comprehensive weight is represented by
The synthetic weights weight values sequencing table of table 12
Best of breed Optimized Measures are scheme S11 as can be seen from the above table, successively sequence be S8, S9, S7, S10, S5, S4,
S1、S2、S6、S3。
12nd step：It is ranked up according to the size of synthetic weights weight values, the scheme set V={ v arranged from big to small1,
v2,...,vn}.The wherein maximum scheme of synthetic weights weight values is best of breed Optimized Measures.Schemes synthesis weight calculation table 12 can
Know.
13rd step：By original measure scheme sn(n=1,2 ..., 11) bring into PCSWMM models and simulated respectively
Checking, will different index Bt (t=1,2 ..., 7) under the conditions of analog result sort, analog result sort with utilize Triangle Module
Paste layer fractional analysis (TFN-AHP) obtains scheme weights omega under the conditions of calculating using indicator layer as criterioniSequence matches, then illustrates
This method is effective, feasible, so that according to comprehensive weight set V={ v1,v2,...,vnObtain final optimal sponge city
LID combined measure prioritization schemes.
By being that criterion condition obtains weight sequencing situation as checking case using " B2 rain peak quantity curtailment rate ", utilize
PCSWMM models, which carry out simulation calculating, can obtain the outflow change curve (Fig. 3) under the conditions of different schemes measure, and
Conversion obtains rain peak reduction rate block diagram (Fig. 4)." B2 rain peak stream is obtained from PCSWMM model simulation results and above-mentioned appraisal procedure
Amount reduction rate " is substantially coincide for the scheme weight sequencing situation that criterion is calculated, it can thus be concluded that being based on TFN-AHP methods to being somebody's turn to do
Sponge city LID measure best of breed optimization evaluation methods be correct.
Claims (1)
1. a kind of sponge city LID measure best of breed optimization evaluation methods based on TFN-AHP methods, it is characterised in that including
Following steps：
The first step：Set up individual event sponge city LID measures ratio and select system
By using the LID measures of individual event sponge city in terms of technical (A1), economy (A2) are with social benefit (A3) index
Deployment analysis.Technical index is mainly divided cuts down including run-off reduction rate (B1), rain peak quantity curtailment rate (B2), runoff pollution
Three kinds of indexs of rate (B3), technical index represents that run-off reduction rate index is represented with B1 with A1, rain peak quantity curtailment rate index
With B2, runoff pollution reduction rate index is represented with B3, i.e. A1={ B1, B2, B3 }；Economic index mainly includes capital construction cost
(B4), operation and two kinds of indexs of maintenance cost (B5), economic indicator represents that the capital construction indicator of costs is represented with B4 with A2, operation and
Maintenance cost represents with B5, i.e. A2={ B4, B5 }；Social benefit index mainly includes landscape effect (B6), ecological functions (B7)
Two kinds of indexs, social benefit index represents that landscape effect is represented with B6 with A3, and ecological functions are represented with B7, i.e. A3=B6,
B7}；The LID measures of sponge city mainly include Green Roof (O1), permeable pavement (O2), concave herbaceous field (O3), rainwater storage
Pond (O4), sponge city LID measures represent that Ox represents each individual event LID measures, wherein x=1, such as 2,3,4, Green Roof use with T
O1 represents that permeable pavement is represented with O2, and concave herbaceous field is represented with O3, and storm detention tank is represented with O4, i.e. Z=O1, O2, O3,
O4}；The LID measures of each single item sponge city are to that should have B1-B7 index；
LID measures index ratio in individual event sponge city selects table table 1；
The individual event sponge city LID measures index ratio of table 1 selects table
Second step：The LID measures of individual event sponge city are subjected to various combination, different measure assembled scheme is formed.With regard to current four kinds
Measure forms 11 kinds of various combination schemes, and wherein Green Roof+permeable pavement represents that Green Roof+up concave type is green with scheme S1
Ground represents that Green Roof+storm detention tank is represented with scheme S3 with scheme S2, and permeable pavement+concave herbaceous field scheme S4 is represented,
Permeable tile work+storm detention tank represents that concave herbaceous field+storm detention tank is represented with scheme S6 with scheme S5, Green Roof+thoroughly
Water tile work+concave herbaceous field represents that Green Roof+permeable tile work+storm detention tank is represented with scheme S8, permeable paving with scheme S7
Brick+concave herbaceous field+storm detention tank represents with scheme S9, Green Roof+concave herbaceous field+storm detention tank scheme S10
Represent, Green Roof+permeable pavement+concave herbaceous field+storm detention tank is represented with scheme S11；
3rd step：Build sponge city LID combined measure schemes Recurison order hierarchy analysis system.The rank step analysis system is four
Layer, including destination layer, rule layer, indicator layer and solution layer；I.e. destination layer is estimated default or to be obtained indicator layer
In index request；Rule layer is technical (A1), economy (A2) and social effect (A3)；Indicator layer is run-off reduction rate
(B1), rain peak quantity curtailment rate (B2), runoff pollution reduction rate (B3), capital construction cost (B4), operation and maintenance cost (B5), scape
See effect (B6) and ecological functions (B7)；Solution layer is scheme S1：Green Roof+permeable pavement, scheme S2：Green Roof+
Concave herbaceous field, scheme S3：Green Roof+storm detention tank, scheme S4：Permeable pavement+concave herbaceous field, scheme S5：It is permeable
Tile work+storm detention tank, scheme S6：Concave herbaceous field+storm detention tank, scheme S7：Green Roof+permeable tile work+up concave type
Greenery patches, scheme S8：Green Roof+permeable tile work+storm detention tank, scheme S9：Permeable tile work+concave herbaceous field+rainwater storage
Pond, scheme S10：Green Roof+concave herbaceous field+storm detention tank, scheme S11：Green Roof+permeable pavement+up concave type is green
Ground+storm detention tank；Each single item scheme is to that should have B1-B7 index；
4th step：Fuzzy judgment matrix is constructed using Triangular Fuzzy Number
(1) fuzzy set is constructed；If domain T, T are in any mapping of closed interval [0,1]Then
A T fuzzy subset is determined, abbreviation fuzzy set is denoted as Referred to as fuzzy setMembership function,It is element
T is under the jurisdiction ofDegree, referred to as degree of membership；
(2) Triangular Fuzzy Number concept；In order to represent that a certain feature x is under the jurisdiction of set M degree, typically conventional triangular function
Express fuzzy membership functions, for any Triangular Fuzzy Number characteristic value between 3 points of x=[l, m, u].IfIt is one
Individual triangle ambiguity function, then its membership function be
Wherein, l≤m≤u, and x ∈ R | l ＜ x ＜ u },
(3) fuzzy judgment matrix is constructed.System and sponge city LID combined measures are selected using individual event sponge city LID measures ratio
Scheme Recurison order hierarchy analyzes system, by expert with the index Bt of indicator layer (t=1,2 ..., be 7) to own in criterion other side's pattern layer
Scheme carry out two-by-two important ratio compared with, and using Triangular Fuzzy Number construct fuzzy judgment matrix；
Order has n (n=1,2 ..11) individual evaluation index, then the judgment matrix constructed is B=(bij)n×n, wherein bij=[lij,mij,
uij] it is with mijFor the closed interval of median, andbijRepresent B under the conditions of a certain criterioniTo BjPhase
The numerical value of importance is embodied, compares and selects table, usual b according to for the individual event sponge city LID measures index ratio of table 1ijUse 1-9
Scaling law, its implication is as shown in table 2.
The B of table 2iAnd BjCorresponding index i and j 1-9 scaling laws and its implication in comparing
Judge comparison, b are carried out provided with H expertsijIt is then comprehensive Triangular Fuzzy Number, wherein
(h=1,2 ..., H), and bij h=[lij h,mij h,uij h] it is the Triangular Fuzzy Number that h-th of expert provides；
5th step：Utilize judgment matrix construction fuzzy evaluation factor matrix E.Computational methods are as follows：
Wherein,For standard profit rate, its value reflects the fog-level that expert judging compares；
6th step：Calculate adjustment judgment matrix Q
In formula, matrix M is the fuzzy median m of all triangles in judgment matrixijThe matrix of composition；
7th step：Will adjustment judgment matrix Q=(qij)n×nThe judgment matrix that diagonal is 1 is converted into by row, judgment matrix is designated as
P, then P=(pij)n×n, and meetAs i >=j,As i ＜ j,
8th step：Line translation is entered to matrix P using consistent matrix analytic approach, consistent matrix R=(r are obtainedij)n×n, R meets consistent
Property condition rij=rik·rkj(k=1,2 ... 11), and rii=1,Meet simultaneously
9th step：Calculate in indicator layer each index respectively in solution layer scheme weights omegaα—Si；Wherein (α=B1, B2, B3, B4, B5, B6, B7；I=1,2,3..., 11).
Tenth step：Calculate comprehensive weight ω of each index for general objective；First, it is determined that suitable for the influence index of survey region
Index request i.e. in estimated default or to be obtained indicator layer, calculates the corresponding different weights of index for obtaining indicator layer
ωα(α=Bi, Bi=B1, B2, B3, B4, B5, B6, B7), computational methods can same ωα—Si；According to trying to achieve index weights and index
Under the conditions of scheme weight carry out comprehensive weight calculating, then(α=B1, B2, B3, B4, B5, B6, B7；i
=1,2,3..., 11)；Indicator layer weight and each scheme weighted value calculate obtaining comprehensive weight value, and entered
Row sequence, selects best of breed Optimized Measures；
11st step：It is ranked up according to the size of synthetic weights weight values, the scheme set V={ v arranged from big to small1,v2,...,
v11}；The wherein maximum scheme of synthetic weights weight values is best of breed Optimized Measures.
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