CN109272189A - A kind of City-scale Flooding Risk appraisal procedure based on chain structure - Google Patents
A kind of City-scale Flooding Risk appraisal procedure based on chain structure Download PDFInfo
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Abstract
The City-scale Flooding Risk appraisal procedure based on chain structure that the invention discloses a kind of, include the following steps: (1) building based on the Urban Flood Waterlogging risk evaluation model of chain structure: then Flood inducing factors needed for determining chain structure, pregnant calamity environment and the big driven factor of hazard-affected body three utilize three big driven factors and its corresponding Index Establishment serial structure, sector structure and parallel organization relationship；(2) it determines and quantifies Flood inducing factors index, pregnant calamity environmental index and hazard-affected body index；(3) the Urban Flood Waterlogging risk model based on chain structure calculates Urban Flood Waterlogging value-at-risk, and carries out rank zoning to risk.The method of the present invention is all of great significance for city flood control, disaster alarm and prevention, urban planning, drainage reconstruction etc., can provide a degree of guidance to reduce the even casualties of economic loss caused by Urban Flood Waterlogging.
Description
Technical field
The present invention relates to Urban Flood Waterlogging risk assessment field, especially a kind of City-scale flood based on chain structure
Damage caused by waterlogging does harm to methods of risk assessment.
Background technique
With the acceleration of Global climate change and urbanization process, the extreme flood in city becomes more frequency
Numerous, coverage is also gradually expanded.China's urban flooding problem also becomes increasingly severe.Except the more south ground of rainfall
Outside area, part northern area urban flooding problem is also very prominent, as Pekinese " 7.21 " waterlogging event causes 79 people
It is dead.As it can be seen that urban flooding problem has seriously threatened the economic normal development in China, carrying out the assessment of urban storm flooding risk has
Important realistic meaning.
Due to space scale difference, the Flooding Risk assessment of urban units and the risk assessment of Watershed Unit can exist
It is significantly different, in addition to used in selecting index, system construction, result etc. exist it is different other than, there is also larger differences for appraisal procedure.
For at present, although there are many method of flood space risk assessment, such as fuzzy overall evaluation, analytic hierarchy process (AHP), collection pair
Analytic approach etc., such method can preferably solve non-linear and uncertain problem, but still come with some shortcomings, such as risk mechanism
It is indefinite, need to be arranged weight and classification standard etc..It is indefinite perhaps for basin or greater room unit, Risk Forming Mechanism
It is smaller on final assessment result influence, but for city even more junior unit, need to consider the formation mechenism problem of risk.And it is right
In weight and classification standard, evaluation result is made because being related to more subjective factor, and often there are one to make with actual conditions
Enter.With the development of technology, the introducing of intelligent algorithm can preferably solve the problems, such as weight and classification standard, but how utilize this
The flood assessment models of a little algorithm building science, are a difficult point of theory of calamity research so far.
Summary of the invention
It is an object of the invention to overcome shortcoming and deficiency in the prior art, provide a kind of based on chain structure flood and waterlog
Evil methods of risk assessment, this method reflect urban flooding risk distribution from three Flood inducing factors, pregnant calamity environment and hazard-affected body levels
Rule is all of great significance for city flood control, disaster alarm and prevention, urban planning, drainage reconstruction etc., Neng Gouwei
It reduces the even casualties of economic loss caused by Urban Flood Waterlogging and a degree of guidance is provided.
In order to achieve the above object, the present invention adopts the following technical scheme that:
A kind of Urban Flood Waterlogging methods of risk assessment based on chain structure, includes the following steps:
Step (1): it constructs the Urban Flood Waterlogging risk evaluation model based on chain structure: determining needed for chain structure
Then Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three utilize three big driven factors and its corresponding Index Establishment string
Row structure, sector structure and parallel organization relationship；
Step (2): it determines and quantifies Flood inducing factors index, pregnant calamity environmental index and hazard-affected body index；
Step (3): the Urban Flood Waterlogging risk model based on chain structure calculates Urban Flood Waterlogging value-at-risk, and right
Risk carries out rank zoning.
As a preferred technical solution, in step (1), the Flood inducing factors refer to the factor for causing waterlogging；The pregnant calamity
Environment refers to the environment for breeding disaster generation；The hazard-affected body refers to the object hit by Flood inducing factors；Pass between three
System shows as chain structure risk, i.e., only Flood inducing factors have occurred and act on pregnant calamity environmentally, and the hazard-affected body on region is
It is possible that being hit；
The chain structure includes three basic structures, i.e. serial structure, sector structure and parallel organization；Chain structure
Main body forms serial structure, and the main body of the chain structure is Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three；Three
The index of big driven factor forms sector structure with corresponding driven factor.
The serial structure refers to that risk source carries out transmitting in sequential order and shadow step by step as a preferred technical solution,
It rings, it is assumed that the value-at-risk of each risk source is R (xi), then the transmitting expression formula of serial risk are as follows:
As long as the sector structure refers to that lower layer's risk can be transmitted to when having a risk to occur in lower layer's risk source
On the risk source of layer, it is assumed that the value-at-risk of each risk source is R (xi), then the transmitting expression formula of fan-shaped risk are as follows:
The parallel organization refers to two or more risk sources parallel while carrying out, and these risk sources are to each other
It is independent of each other, it is assumed that the value-at-risk of each risk source is R (xi), then the transmitting expression formula of parallel risk are as follows:
Y=f (x1,x2,...,xn)=max [R (xi)] (3)
In formula (1)~formula (3), xiIndicate i-th of risk source；R(xi) indicate xiValue-at-risk；N indicates the total of risk source
Number.
The Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three composition are serial as a preferred technical solution,
The index of structure, three big driven factors forms sector structure with corresponding driven factor；The danger of the Flood inducing factors reflection flood
Property, the stability that pregnant calamity environment reflection flood is formed, and hazard-affected body then embodies the fragility of flood；Building based on chain structure
Urban Flood Waterlogging risk evaluation model, calculation formula is as follows:
In formula, ziFor i-th of driven factor, i.e. expression risk source, m is the index number of corresponding driven factor, wjFor jth
The corresponding weight of a index；VjFor the standardized value of j-th of index, calculated with specific reference to following formula:
Vj=(V'j-V'min)/(V'max-V'min) or Vj=(V'max-V'j)/(V'max-V'min) (5)
In formula, V'jIndicate the numerical value before criterion, V'maxFor the maximum value before criterion；V'minTo refer to
Minimum value before mark standardization, VjFor the numerical value after criterion.
As a preferred technical solution, in step (2), the determining Flood inducing factors index: rainfall and rainfall collection are chosen
Moderate two indices；The rainfall concentration degree can measure heterogeneity of the precipitation in time scale in the period；Pass through calculating
Rainfall analysis same day rainfall concentration degree, the uniform rainfall of rainfall are conducive to rainwater and are transported to drainage pipeline；And precipitation is excessively
It concentrates, short duration rainfall can increase, and bring severe challenge to Urban Waterlogging ability；
The pregnant calamity environmental index of determination: digital elevation model, the gradient, pipe network coverage rate, pumping drainage ability, diameter are chosen
It flows coefficient and arrives six indexs of river distance；The digital elevation model and the gradient are used to measure topography and geomorphology, wherein the gradient
It is extracted using digital elevation model, the Regional Flooding risk that elevation is lower, the gradient is more slow is higher, otherwise lower；The pipe network covers
For measuring pipe network level of coverage and pumping plant evacuation ability, the two numerical value shows more greatly water drainage ability for lid rate and pumping drainage ability
Stronger, the threat attacked by flooding risk is smaller；The runoff coefficient show that different soils are sharp based on land use pattern
It is different with ability is seeped under the soil of type；It is described arrive river distance, the value show more greatly it is remoter from river water system, be less susceptible to by
River flood influences；
The determining supporting body index: choosing the density of population and GDP density two indices, the two numerical value show more greatly population
It is more concentrated with property distribution, the risk by flood danger is bigger.
As a preferred technical solution, in step (2), to ten indexs of Flood inducing factors, pregnant calamity environment and hazard-affected body into
Row quantization: the rainfall, digital elevation model, the gradient, pipe network coverage rate, pumping drainage ability, runoff coefficient, to river away from
Rainfall website data, digital elevation model figure, pipe network data, soil benefit can be passed through from, nine indexs of the density of population and GDP density
Directly or simple process obtains with type distribution, water system sediments figure, the density of population and GDP statistical data；The rainfall concentration degree
It is calculated by the following formula:
In formula, PC is rainfall concentration degree, piFor the rainfall of unit period, n indicates the period sum of this rainfall.
As a preferred technical solution, in step (3), using ArcGIS10.2 raster symbol-base device, calculated according to formula (4)
Urban Flood Waterlogging value-at-risk, and risk class zoning is carried out using percentile classification.
The present invention has the following advantages compared with the existing technology and effect:
1) " Flood inducing factors-pregnant calamity environment-hazard-affected body " three big factors are the main of influence Urban Flood Waterlogging risk
Factor, the present invention specifies the serial chain structure between these three factors, and each index is sector structure with the corresponding factor,
It is more in line with the formation mechenism of urban area Flooding Risk, so that assessment result be made more to be truly reflected actual conditions；
2) other calamity source assessment models, the calculating of chain structure Flooding Risk assessment models of the invention are compared
Formula is more simple and convenient, and each index value is easy in conjunction with GIS, so the evaluation model is easy through GIS technology reality
It is existing.
Detailed description of the invention
Fig. 1 is the Urban Flood Waterlogging methods of risk assessment flow chart based on chain structure of the embodiment of the present invention；
Fig. 2 (a), Fig. 2 (b), Fig. 2 (c) are respectively that serial structure, sector structure, the parallel organization of the embodiment of the present invention show
It is intended to；
Fig. 3 is Shenzhen's chain structure Flooding Risk assessment models of the embodiment of the present invention；
Fig. 4 (a)~Fig. 4 (j) is 10 risk indicator spatial distribution maps of the embodiment of the present invention；Wherein Fig. 4 (a), Fig. 4
(b), Fig. 4 (c), Fig. 4 (d), Fig. 4 (e), Fig. 4 (f), Fig. 4 (g), Fig. 4 (h), Fig. 4 (i), Fig. 4 (j) be respectively rainfall (RA),
Rainfall concentration degree (PC), digital elevation model (DEM), the gradient (SL), pipe network coverage rate (NC), pumping drainage ability (PW), diameter
Flow coefficient (RC) and the spatial distribution map to river distance (DR), the density of population (POP) and GDP density (GDP)；
Fig. 5 is Shenzhen's Flooding Risk figure based on chain structure of the embodiment of the present invention；
Fig. 6 is Shenzhen's easily flooded area distribution schematic diagram in 2011 of the embodiment of the present invention.
Specific embodiment
In order to which the purpose of the present invention, technical solution and advantage is more clearly understood, with reference to the accompanying drawings and embodiments,
The present invention is further described in detail.It should be appreciated that described herein, the specific embodiments are only for explaining the present invention,
It is not limited to the present invention.
Embodiment
As shown in Figure 1, a kind of Urban Flood Waterlogging methods of risk assessment based on chain structure, includes the following steps:
Step (1): it constructs the Urban Flood Waterlogging risk evaluation model based on chain structure: determining needed for chain structure
Then Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three utilize three big driven factors and its corresponding Index Establishment string
Row structure, parallel organization and sector structure relationship；
Step (2): it determines and quantifies Flood inducing factors index, pregnant calamity environmental index and hazard-affected body index；
Step (3): the Urban Flood Waterlogging risk model based on chain structure calculates Urban Flood Waterlogging value-at-risk, and right
Risk carries out rank zoning.
The following are the present embodiment to the detailed description of technical solution of the present invention
1, chain structure principle
For the chain structure risk model in step (1), generally there is following 3 kinds of basic structure: serial structure risk, fan
Shape structure risk and parallel organization risk, respectively as shown in Fig. 2 (a), Fig. 2 (b), Fig. 2 (c).The Risk mode of some complexity
Mainly it is made of the basic mechanism of three of the above；
The serial structure refers to that risk source is transmitted in sequential order and influenced step by step, it is assumed that the wind of each risk source
Danger value is R (xi), then the transmitting expression formula of serial risk are as follows:
As long as the sector structure refers to that lower layer's risk can be transmitted to when having a risk to occur in lower layer's risk source
On the risk source of layer, it is assumed that the value-at-risk of each risk source is R (xi), then the transmitting expression formula of fan-shaped risk are as follows:
The parallel organization refers to two or more risk sources parallel while carrying out, and these risk sources are to each other
It is independent of each other, it is assumed that the value-at-risk of each risk source is R (xi), then the transmitting expression formula of parallel risk are as follows:
Y=f (x1,x2,...,xn)=max [R (xi)] (3)
In formula (1)~formula (3), xiIndicate i-th of risk source；R(xi) indicate xiValue-at-risk；N indicates the total of risk source
Number.
2, chain type Flooding Risk assessment models
For constructing the Urban Flood Waterlogging risk evaluation model based on chain structure: chain structure main body in step (1)
Including Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three, and serial structure is formed, each index of three big driven factors
Sector structure is formed with corresponding driven factor；The Flood inducing factors are the principal elements for causing waterlogging, and pregnant calamity environment is to breed calamity
The environment that evil occurs, hazard-affected body are then the objects hit by Flood inducing factors, and the relationship between three should show as chain structure
Risk, i.e., only Flood inducing factors have occurred and act on pregnant calamity environmentally, and the hazard-affected body on region is possible to be hit.If
There is no the effect of Flood inducing factors, flood and waterlog will not occur possessing again severe pregnant calamity environment and most intensive hazard-affected body
Evil；If even if Flood inducing factors are acted on, but since pregnant calamity environment is good, not forming flood, will not being produced to supporting body
Raw larger impact；If even if Flood inducing factors are had an effect, and flood has occurred under severe pregnant calamity environment, but not hazard-affected
Body will not generate disaster.It can be seen that the formation of flood, three big driven factors are the results of interaction all linked with one another.
As shown in figure 3, building Shenzhen's chain structure Flooding Risk assessment models, index and driven factor constitute one
Kind sector structure, and serial structure is then formed between three big driven factors.Flood inducing factors reflect the risk of flood, pregnant calamity ring
Border reflects the stability of flood formation, and hazard-affected body then embodies fragility.The chain type Flooding Risk assessment models of proposition
Calculation formula is as follows:
In formula, ziFor i-th of driven factor, i.e. expression risk source, m is the index number of corresponding driven factor, wjFor jth
The corresponding weight of a index；VjFor the standardized value of j-th of index, calculated with specific reference to following formula:
Vj=(V'j-V'min)/(V'max-V'min) or Vj=(V'max-V'j)/(V'max-V'min) (5)
In formula, V'jIndicate the numerical value before criterion, V'maxFor the maximum value before criterion；V'minTo refer to
Minimum value before mark standardization, VjFor the numerical value after criterion.
3, index system establishment
According to Disaster System theory, it then follows representative, objective and accurate property, systematicness, easily obtain with the principles such as easy to operate,
From Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three 10 indexs of total selection, i.e., in step (2) determining three
The index of big driven factor is described in detail.
Flood inducing factors: rainfall (RA) and rainfall concentration degree (PC) two indices are chosen.Rainfall concentration degree (PC) is intended to weigh
Measure the period in heterogeneity of the precipitation in time scale, be generally used for measure month by month, year it is isometric last rainfall concentrate situation.This
The concentration degree concept is quoted in invention, by calculating hourly rainfall amount analysis same day rainfall concentration degree.In general, rainfall is uniform
Rainfall, be conducive to rainwater and be transported to drainage pipeline；And precipitation is excessively concentrated, short duration rainfall can increase, and give Urban Waterlogging energy
Power brings severe challenge, and analysis rainfall concentration degree is of great significance to the formation for dissecting urban waterlogging.
Pregnant calamity environment: digital elevation model (DEM), the gradient (SL), pipe network coverage rate (NC), pumping drainage ability are chosen
(PW), runoff coefficient (RC) and to river distance (DR) six indexs.DEM (30m) and SL is utilized for measuring topography and geomorphology, SL
DEM is extracted.In general, the Regional Flooding risk that elevation is lower, the gradient is more slow is higher, otherwise lower.NC and PW is for measuring
Pipe network level of coverage and pumping plant evacuation ability, the two numerical value show that more greatly water drainage ability is stronger, attack by flooding risk
It threatens smaller.RC show that the lower infiltration ability of different land use type is different based on land use pattern, is assigned respectively according to table 1
The runoff coefficient of kind land use pattern.DR indicates to river distance, and value shows more greatly remoter from river water system, is less susceptible to
It is influenced by river flood.Therefore, pregnant calamity environment more reflects the overview of underlying surface.
The corresponding runoff coefficient of 1 different land use type of table
Hazard-affected body: the density of population (POP) and GDP density (GDP) are used as the hazard-affected body factor, and value shows more greatly population and wealth
Distribution is produced more to concentrate, it is bigger by flood danger risk.
In 10 selected indexs, DEM, SL, NC, PW and DR are negative index, remaining is direct index.
4, quantify various indexs
In 10 indexs of Flood inducing factors, pregnant calamity environment and hazard-affected body, rainfall (RA), digital elevation model (DEM),
The gradient (SL), pumping drainage ability (PW), runoff coefficient (RC), arrives river distance (DR), the density of population at pipe network coverage rate (NC)
(POP) and nine indexs needs of GDP density (GDP) can be with rainfall website data, DEM figure, pipe network data, land use pattern point
Cloth, water system sediments figure, the density of population and GDP statistical data are directly or simple process obtains, but rainfall concentration degree needs in detail
Thin definition and calculating.
Rainfall concentration degree (PC) shows that more greatly the rainfall in the short time is bigger, and concentration degree is smaller to show that rainfall is more equal
Even, value can be calculated with following formula:
In formula, PC is rainfall concentration degree；piFor the rainfall of unit period, n indicates the period sum of this rainfall.PC number
Value can be by three classes be divided as follows, and classification is higher to show that rainfall is more uneven, and rainfall rainfall in certain short duration is bigger, and table 2 is rainfall
Concentration degree classification chart.
2 PC grade classification standard of table
Based on above-mentioned chain structure Flooding Risk assessment models, Flooding Risk is assessed by taking Shenzhen as an example, is had
Body applying step is as follows:
(1) quantify rainfall (RA) and two Flood inducing factors indexs of concentration degree (PC): according to the typical waterlogging thing in 25, Shenzhen
Part rainfall percentile (table 3) finds that maximum 1h, maximum 6h and same day rainfall percentile are all larger than on the day of most of waterlogging event
99%, it is believed that the critical rainfall for ranking 99% is one important " playing flooded point ".By the maximum 1h of interpolation ranking 99%, most
After big 6h and same day rainfall, discovery three's spatial distribution is almost consistent, final to choose ranking and represent for 99% rainfall in one day
Interception rainfall index.By Shenzhen's waterlogging Rainstorm Feature it is found that rainfall concentration degree on waterlogging formed influence it is very big.The index screening goes out 10
It is a represent daily rainfall in website be greater than ranking for 99% all rainfall plays and calculate its rainfall concentration degree, take and be averaged for many years
Value is used as rainfall concentration degree index, can obtain the whole city by space interpolation and be averaged rainfall concentration degree spatial distribution, such as Fig. 4 (a) and Fig. 4
(b).The rainfall in one day and rainfall concentration degree that ranking in Flood inducing factors is 99% can reflect under changing environment in recent decades
Characteristics of rainfall, i.e. changing environment may be embodied on Flood inducing factors.
The typical flood event rainfall percentile unit of table 3: mm
Note: it chooses sample of the rainfall greater than 1mm and is counted for percentile.
The typical rainfall for causing waterlogging according to 25 calculates the rainfall concentration degree of each play using formula (6), such as
Shown in table 4.
The typical flood event rainfall concentration degree (PC) of table 4
5 PC rank frequency of table and ratio
As shown in Table 4, the big rainfall concentration degree of same day rainfall is typically small, shows that length lasts, uniformly measures big drop
Rain is such waterlogging main cause；The lesser concentration degree of same day rainfall is larger instead, shows that short duration high-intensity rainfall is this
The main reason for class waterlogging.As shown in Table 5, rainfall concentration degree be ratio shared by I grade, II grade and III grade respectively reach 24%,
36% and 40%, it is mainly reflected in relatively to concentrate (II grade) in 25 typical rainfalls and concentrates (III grade).
(2) quantify groundwater: digital elevation model (DEM), the gradient (SL), pipe network coverage rate (NC), pumping plant row
Outlet capacity (PW), runoff coefficient (RC) and spatial distribution such as Fig. 4 (c)~Fig. 4 (h) to river distance (DR) six index values.
Pregnant calamity environment more reflects the overview of underlying surface, and snafu variation has occurred in Shenzhen's underlying surface in recent decades,
I.e. changing environment can also be embodied in pregnant calamity environmentally.
(3) quantify hazard-affected body: the density of population (POP) and GDP density (GDP) take 2010 annual datas, spatial distribution
Such as Fig. 4 (i) and Fig. 4 (j).
(4) ArcGIS10.2 raster symbol-base device is utilized, calculates Shenzhen's Flooding Risk value, and benefit according to formula (4)
Risk class zoning is carried out with percentile classification, as a result as shown in figure 5, it can be seen that high risk area is concentrated mainly on treasured
The middle and south An Qu, Nanshan District south, the Futian District middle and south, Luohu District western part, Longgang District the north, Pingshan new district middle part and roc
In the middle part of new district.Each index space distribution map of comparison diagram 4 (a)~Fig. 4 (j) is it is found that high risk area is mainly characterized in that hypsography low-lying, slope
Degree is gentle, thus be easy to cause ponding；And these ponding regions are often the lower built-up areas of permeability rate, the density of population and GDP
Density is bigger, thus leads to high risk.
(5) for the reasonability of proof diagram 5, using " Shenzhen's easily flooded area distribution schematic diagram in 2011 " as verifying.
The figure is waterlogging submergence ratio figure caused by statistics more rainfalls in 2011, does offer by Shenzhen's three proofings, is specifically shown in Fig. 6 institute
Show.Both comparisons are it is found that Fig. 6 either waterlogging is serious or more serious waterlogging region, most of high risk with Fig. 5 or higher wind
Danger zone domain matches.But Baoan District is northern, there is some difference in regional area in the middle part of Longgang District, and main cause may be: 1)
Fig. 5 is the Flooding Risk figure of entire Shenzhen, and Fig. 6 has then only counted the submergence ratio for causing heavy losses, some
The waterlogging for betiding the regions such as wasteland, marsh and meadow does not count；2) Fig. 6 has only counted flood in 2011 and floods model
It encloses, there is certain contingency, potential risk area is possible to not be identified, thus can not represent Shenzhen's waterlogging completely
Situation is flooded, and Fig. 5 is the calamity source for considering many years comprehensive condition.Despite the presence of certain difference, but on the whole, figure
6 can preferably verify the reasonability based on chain structure Shenzhen Flooding Risk figure.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.Therefore, the scope of protection of the patent of the present invention should subject to the claims.
Claims (7)
1. a kind of Urban Flood Waterlogging methods of risk assessment based on chain structure, which is characterized in that include the following steps:
Step (1): the Urban Flood Waterlogging risk evaluation model based on chain structure: cause calamity needed for determining chain structure is constructed
Then the factor, pregnant calamity environment and the big driven factor of hazard-affected body three are serially tied using three big driven factors and its corresponding Index Establishment
Structure, sector structure and parallel organization relationship；
Step (2): it determines and quantifies Flood inducing factors index, pregnant calamity environmental index and hazard-affected body index；
Step (3): the Urban Flood Waterlogging risk model based on chain structure calculates Urban Flood Waterlogging value-at-risk, and to risk
Carry out rank zoning.
2. the Urban Flood Waterlogging methods of risk assessment according to claim 1 based on chain structure, which is characterized in that step
Suddenly in (1), the Flood inducing factors refer to the factor for causing waterlogging；The pregnant calamity environment refers to the environment for breeding disaster generation；Institute
It states hazard-affected body and refers to the object hit by Flood inducing factors；Relationship between three shows as chain structure risk, i.e., only causes
The calamity factor has occurred and acts on pregnant calamity environmentally, and the hazard-affected body on region is possible to be hit；
The chain structure includes three basic structures, i.e. serial structure, sector structure and parallel organization；The main body of chain structure
Serial structure is formed, the main body of the chain structure is Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three；Three big drives
The index of reason forms sector structure with corresponding driven factor.
3. the Urban Flood Waterlogging methods of risk assessment according to claim 2 based on chain structure, which is characterized in that institute
It states serial structure and refers to that risk source is transmitted in sequential order and influenced step by step, it is assumed that the value-at-risk of each risk source is R
(xi), then the transmitting expression formula of serial risk are as follows:
As long as the sector structure refers to that lower layer's risk can be transmitted to upper layer when having a risk to occur in lower layer's risk source
On risk source, it is assumed that the value-at-risk of each risk source is R (xi), then the transmitting expression formula of fan-shaped risk are as follows:
The parallel organization refers to two or more risk sources parallel while carrying out, and these risk sources are to each other mutually not
It influences, it is assumed that the value-at-risk of each risk source is R (xi), then the transmitting expression formula of parallel risk are as follows:
Y=f (x1,x2,...,xn)=max [R (xi)] (3)
In formula (1)~formula (3), xiIndicate i-th of risk source；R(xi) indicate xiValue-at-risk；The sum of n expression risk source.
4. the Urban Flood Waterlogging methods of risk assessment according to claim 2 based on chain structure, which is characterized in that institute
State Flood inducing factors, pregnant calamity environment and the big driven factor of hazard-affected body three composition serial structure, the index of three big driven factors with it is corresponding
Driven factor forms sector structure；The risk of the Flood inducing factors reflection flood, the stabilization that pregnant calamity environment reflection flood is formed
Property, and hazard-affected body then embodies the fragility of flood；The Urban Flood Waterlogging risk evaluation model based on chain structure of building,
Calculation formula is as follows:
In formula, ziIndicate that i-th of driven factor, i.e. expression risk source, m are the index number of corresponding driven factor, wjIt is j-th
The corresponding weight of index；VjFor the standardized value of j-th of index, calculated with specific reference to following formula:
Vj=(V'j-V'min)/(V'max-V'min) or Vj=(V'max-V'j)/(V'max-V'min) (5)
In formula, V'jIndicate the numerical value before criterion, V'maxFor the maximum value before criterion；V'minFor criterion
Minimum value before change, VjFor the numerical value after criterion.
5. the Urban Flood Waterlogging methods of risk assessment according to claim 1 based on chain structure, which is characterized in that step
Suddenly in (2), rainfall and rainfall concentration degree two indices the determining Flood inducing factors index: are chosen；The rainfall concentration degree energy
Enough measure heterogeneity of the precipitation in time scale in the period；By calculating rainfall analysis same day rainfall concentration degree, rainfall
Uniform rainfall is measured, is conducive to rainwater and is transported to drainage pipeline；And precipitation is excessively concentrated, short duration rainfall can increase, and give city
Water drainage ability brings severe challenge；
The pregnant calamity environmental index of determination: digital elevation model, the gradient, pipe network coverage rate, pumping drainage ability, runoff system are chosen
Count and arrive six indexs of river distance；The digital elevation model and the gradient are for measuring topography and geomorphology, and wherein the gradient utilizes
Digital elevation model extracts, and the Regional Flooding risk that elevation is lower, the gradient is more slow is higher, otherwise lower；The pipe network coverage rate
With pumping drainage ability for measuring pipe network level of coverage and pumping plant evacuation ability, the two numerical value shows more greatly water drainage ability more
By force, smaller by the threat of flooding risk attack；The runoff coefficient obtained based on land use pattern, different land use
It is different that ability is seeped under the soil of type；Described to arrive river distance, which shows more greatly remoter from river water system, is less susceptible to by river
Road flood influence；
The determining supporting body index: choosing the density of population and GDP density two indices, the two numerical value show more greatly population and wealth
It produces distribution more to concentrate, the risk by flood danger is bigger.
6. the Urban Flood Waterlogging methods of risk assessment according to claim 5 based on chain structure, which is characterized in that step
Suddenly in (2), ten indexs of Flood inducing factors, pregnant calamity environment and hazard-affected body are quantified: the rainfall, digital elevation mould
Type, pipe network coverage rate, pumping drainage ability, runoff coefficient, arrives nine river distance, the density of population and GDP density indexs at the gradient
It can be close by rainfall website data, digital elevation model figure, pipe network data, land use pattern distribution, water system sediments figure, population
Degree and GDP statistical data are direct or simple process obtains；The rainfall concentration degree is calculated by the following formula:
In formula, PC is rainfall concentration degree, piFor the rainfall of unit period, n indicates the period sum of this rainfall.
7. the Urban Flood Waterlogging methods of risk assessment according to claim 4 based on chain structure, which is characterized in that step
Suddenly in (3), using ArcGIS10.2 raster symbol-base device, Urban Flood Waterlogging value-at-risk is calculated according to formula (4), and utilize percentage
Position classification carries out risk class zoning.
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|---|---|---|---|---|
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140278561A1 (en) * | 2013-03-14 | 2014-09-18 | Stoneriver National Flood Services, Inc. | Computerized system and method for determining flood risk |
| CN104217257A (en) * | 2014-09-12 | 2014-12-17 | 福建师范大学 | Integrated risk calculating method of disaster chain |
| CN106651211A (en) * | 2016-12-30 | 2017-05-10 | 吉林师范大学 | Different-scale regional flood damage risk evaluation method |
-
2018
- 2018-08-03 CN CN201810878983.7A patent/CN109272189A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140278561A1 (en) * | 2013-03-14 | 2014-09-18 | Stoneriver National Flood Services, Inc. | Computerized system and method for determining flood risk |
| CN104217257A (en) * | 2014-09-12 | 2014-12-17 | 福建师范大学 | Integrated risk calculating method of disaster chain |
| CN106651211A (en) * | 2016-12-30 | 2017-05-10 | 吉林师范大学 | Different-scale regional flood damage risk evaluation method |
Non-Patent Citations (3)
| Title |
|---|
| 潘小军 等: "风险要素及其序惯性链式结构分析", 《风险分析和危机反应的创新理论和方法——中国灾害防御协会风险分析专业委员会第五届年会论文集》 * |
| 翟运开: "基于协同产品创新的区域性产业集群风险传染模式研究", 《工业技术经济》 * |
| 陆仁强 等: "城市供水系统风险传播机理模型研究", 《自然灾害学报》 * |
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