CN110458391A - A kind of fining diagnostic method for Megapolis flood risk zoning - Google Patents
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Abstract
The invention discloses a kind of fining diagnostic methods for Megapolis flood risk zoning, data rasterizing and standardization are carried out to each index using GIS spatial data analytic function, construct judgment matrix corresponding with index system, calculate the relative weighting and difference grid cell flood danger level of all kinds of indexs, it is achieved in the foundation of the Megapolis flood Hazard degree assessment model based on Fuzzy AHP and the drafting of flood risk mapping, to carry out flood risk diagnosis, from distribution of rainfall feature, history big flood characteristic distributions, each urban storm ponding risk distribution feature is angularly, all-dimensional multi-angle system diagnostics Megapolis flood risk distribution characteristics.The present invention, which realizes, accurately calculates different danger area flood danger levels and danger classes, science is objectively by multi objective question synthesis at single index form, the single index that can effectively measure flood risk size is constructed, provides reliable technical support means for flood risk zontation.
Description
Technical field
The present invention relates to emergency and disaster prevention diagnostic techniques field more particularly to the structures of a kind of flood risk assessment and diagnostic method
It builds.
Background technique
Group of cities is to pass through steric interaction shape by the different grades of city and its innerland of several dense distributions
At city domain system.With the quickening of urbanization process, extreme climate frequently occurs, and ground Permeable stratum area constantly increases
Greatly, population and wealth are constantly assembled, and Urban Flood Waterlogging risk is increasing, cause very big prestige to people's production and life
The side of body.Therefore carrying out the flood risk distribution characteristics in flood risk zoning and the Precise Diagnosis region to Megapolis is both city
The important evidence of emergency and disaster prevention and the needs of city risk management and sustainable development.
Flood risk zoning mainly has Principal Component Analysis and analytic hierarchy process (AHP) at present, and both methods is to a certain extent
It is able to reflect the distribution characteristics of flood risk, but there is also some shortcomings, if Principal Component Analysis is in terms of index weights determination
There are out-of-order problems；Analytic hierarchy process (AHP) has the subjective arbitrariness for determining weight.In addition, current flood risk zoning mainly with
The Administrative boundaries such as district divide evaluation unit, can not be accurately positioned flood risk since space scale is excessive.Therefore, in order to perfect
Fire risk district method and the deficiencies of larger evaluation unit scale at present, the present invention provides the fine floods for being directed to Megapolis
The Fuzzy AHP of water fire risk district is so as to accurate and objectively diagnose the flood risk feature of Megapolis
It improves and implements big flood emergency preplan, enhancing to emergency managerial ability of big flood etc. offer scientific basis.
Summary of the invention
It is an object of the invention to propose a kind of fining diagnostic method for Megapolis flood risk zoning, adopt
It is blended, Megapolis flood risk is carried out based on grid technique fine with analytic hierarchy process (AHP) and fuzzy synthetic appraisement method
Zoning and diagnosis, it is intended to the flood risk that urban economy group region carries out flood risk zoning and diagnoses the region is distributed special
Sign provides important reference for research city Flood Hazard Risk and contingency management and the cooperative development of Megapolis.
A kind of city different underlying surface mitigation effect analysis method of the invention, process the following steps are included:
Step 1: building flood risk assessment index system, constructed assessment indicator system is not destination layer, rule layer
With 3 levels of indicator layer, wherein destination layer is flood danger level simple target；Rule layer includes Flood inducing factors, pregnant calamity environment, holds
Calamity body；The setting of indicator layer should be corresponding with rule layer, and Flood inducing factors include that frequency is flooded in rainfall, heavy rain frequency and historical flood
Secondary, hazard-affected body includes height above sea level, terrain slope and basin water system buffer area, and hazard-affected body includes the density of population, GDP per capita and water
Sharp engineering flood control capacity；
Step 2: the data in step 1 are standardized,
It is positive correlation, height above sea level that storm rainfall, historical flood, which flood the frequency, the density of population, GDP per capita and flood danger level,
Elevation, terrain slope, basin water system buffer level, flood control capacity and flood danger level become negative correlativing relation；For negative
Index is closed, its inverse is sought, realizes its conversion between positive correlation index；The standardization of flood danger level achievement data is public
Formula is as follows:
In formula, xiFor each grid point initial data series of certain index, xmaxAnd xminMaximum and minimum value respectively therein；
xi' it is to standardize later value, between 0 to 100；
Step 3: determining evaluation criterion weight: the membership by determining element between upper and lower level establishes Recurison order hierarchy
Structure determines that its is relatively important using the method compared two-by-two to each Index element of the identical membership of same level
Property, thus Judgement Matricies, and it is translated into fuzzy judgment matrix, respective weights value is obtained, following processing is specifically included:
After step 31, recursive hierarchy structure are established, the membership of element has been determined between upper and lower level, extracts flood
Hazard Risk Assessment index；
Step 32 assumes that above one layer of element is criterion, and the next layer of element dominated is u1, u2..., un, using two-by-two
The method compared assigns corresponding weight for the relative importance of upper one layer of element by them, in this way, n is a by comparison element
Construct multilevel iudge matrix two-by-two:
A=(aij)n×n (12)
Wherein, uiWith ujIndicate the lower layer's Index element being compared to each other, aijIndicate element uiWith ujRelative to upper one layer of element
Importance proportion quotiety；
Step 33, according to proportion quotiety conversion formula, convert Fuzzy Complementary Judgment Matrices B for judgment matrixn×n, expression
Formula is as follows:
bij=logαaij+0.5 (13)
Bn×n=(bij)n×n (14)
In formula, bijFor uiWith ujRelative to the transition proportion quotiety of upper layer element,
bijWhen=0.5, uiWith ujImportance is identical；
bijWhen > 0.5, uiCompare ujIt is important；
bijWhen < 0.5, ujCompare uiIt is important, and b need to be metii=0.5, bij+bji=1 α >=81 >=81；
Fuzzy consistent judgment matrix R is converted by Fuzzy Complementary Judgment Matricesn×n,
Rn×n=(rij)n×n (17)
Step 34 asks respective weights vector to calculate each layer evaluation index that is, according to the judgment matrix of consistency check qualification
For the relative weighting w of flood danger level (decision objective)1, w2..., wn, relative weighting can be write as vector form, i.e. and ω=
(W1,W2,...,Wn)T；
Step 35, by fuzzy consistent judgment matrix Rn×nIt is normalized, and uses eigenvalue method, i.e. formula (18),
Find out maximum eigenvalue λmax；
Rn×nω=λmaxω (18)
Step 36 carries out consistency check, that is, passes through maximum eigenvalue λmaxCalculate coincident indicator CI:
Work as CI=0, it is completely the same；CI value is smaller, and fuzzy consistent judgment matrix is more close to safe consistent degree；
To prevent judgment matrix from deviateing safe consistent degree, Aver-age Random Consistency Index RI is introduced, and take CR to measure
The index of matrix consistency:
When CR≤0.1, then it is assumed that the consistency of fuzzy consistent judgment matrix can receive, and be otherwise considered as fuzzy consensus and sentence
Disconnected matrix is unsatisfactory for consistency, needs to be adjusted judgment matrix, until consistency check reaches requirement；
Step 4: establishing flood Hazard degree assessment model and drawing flood risk mapping；
Step 5: carrying out flood risk diagnosis using flood risk mapping.
Compared with the conventional method, the beneficial effects brought by the technical solution of the present invention are as follows:
The present invention is directed to the Flood Characteristics in economic city group region, provides a kind of diagnostic method of flood risk regionalization,
Different danger area flood danger levels and danger classes are accurately calculated, and different danger area distribution of grades are depicted based on GIS platform
Figure visualizes the flood risk and degree of danger distribution characteristics of Megapolis；This method has been evaded analytic hierarchy process (AHP) and has been commented
The disadvantages of ambiguity and subjectivity when things or object that valence is restricted by Multiple factors, greatly optimizes weight calculation step
Suddenly, the unification of judgment matrix approach Yu policymaker's thinking is finally realized；In addition, the method compares flood danger by analysis
Dangerous degree evaluates all kinds of indexs and its shared weight, and science constructs energy objectively by multi objective question synthesis at single index form
Enough single indexs for effectively measuring flood risk size, provide a kind of reliable technical support hand for flood risk zontation
Section.
Detailed description of the invention
Fig. 1 is that a kind of fining diagnostic method overall flow for Megapolis flood risk zoning of the invention is shown
It is intended to；
Fig. 2 is FAHP Fuzzy AHP flow chart in Weight Determination of the invention.
Specific embodiment
Below in conjunction with attached drawing, detailed description of the preferred embodiments.It should be understood that being described herein as
Specific embodiment be only used for illustrate the present invention, be not intended to restrict the invention.
As shown in Figure 1, a kind of diagnostic method flow diagram of flood risk zoning for the application, the process specifically include
Following steps:
Step 1, system of being caused disaster according to dangerous conceptual framework and flood, comprehensively considering, flood risk assessment index system is true
On the basis of fixed purpose, systematicness, science and operability principle, referred to based on GIS platform building flood risk assessment
Mark system, it is respectively destination layer, rule layer and indicator layer that flood risk assessment index system, which is divided into 3 levels i.e., according to finger
Mark system hierarchical relationship, is established as recursive hierarchy structure, is as shown in table 1 " indicator layer, rule layer, destination layer " Recurison order hierarchy knot
Structure model.Wherein destination layer is flood danger level simple target；Rule layer considers flood and causes disaster the basic composition of system, including
Flood inducing factors, pregnant calamity environment and hazard-affected body；And the setting of indicator layer should be corresponding with rule layer, corresponding evaluation index is selected from
The frequency, height above sea level, terrain slope, place water system buffer level, population are flooded including rainfall, heavy rain frequency, historical flood
The data such as density, GDP per capita and engineering flood control standard.These data using GIS spatial data handle with draughtsmanship come collect,
It arranges, such as:
(1) rainfall: according to heavy rain isopleth and the Multi-year average precipitation contour analysis for 24 hours of average year maximum it is found that
The rainfall spatial distribution characteristic that the two embodies is quite similar, reflects the space distribution situation of Rainfall in Flood Seasons substantially.Therefore,
Consider comprehensive embodiment precipitation high intensity and two features of continuity, chooses average year maximum rainfall in 24 h as flood danger level
Evaluation index.
(2) heavy rain frequency: the number of days of heavy rain occurring for certain region every year, reflects this area's Storm Flood Disasters and occurs by force
Degree, expression formula are as follows:
In formula, N indicates heavy rain frequency, TiFor the 1 year torrential rain days in certain region, n is statistical sample year.
(3) frequency is flooded in historical flood: being generated historical flood based on GIS platform and is flooded frequency diagram, clearly shows difference
Grid cell is flooded that the frequency is higher, and flood danger level is higher by the number that historical flood is flooded or is influenced.
(4) it hypo height and terrain slope: according to the dem data that Megapolis is intensive, is handled using GIS spatial data
With draughtsmanship, the absolute elevation figure and terrain slope data and its distribution characteristics of survey region are obtained, the gradient calculates only in GIS
Elevation variation degree between consideration adjacent cells, and actual influence flood danger level size should be a certain range of crust deformation
Change, therefore elevation standard deviation (STD) data in certain neighborhood is used to measure terrain slope, thus obtains landform altitude STD distribution map.
(5) water system buffer level: the foundation such as river, lake, flood storage and detention basin are buffered using GIS buffer zone analysis tool
Area can get basin water system buffer level distribution characteristics.
(6) density of population and GDP per capita are analyzed close to population using administrative areas at the county level as basic statistics unit based on GIS
Degree GDP per capita data carry out digitlization and rasterizing processing, generate the density of population and GDP per capita distribution map, show city with this
Group regional population is intensive and GDP per capita distribution characteristics.
(7) flood control capacity: being handled using GIS spatial data and draughtsmanship carries out Megapolis Actual flood protection ability
Digitlization and grid processing, obtain the Actual flood protection ability distribution map in the region.
Flood inducing factors consider that city flood is usually generated by wet monsoon heavy rain, and storm rainfall has been largely fixed flood
Water danger level size；Historical flood data contains the natural law of flood generation, and history typical flood floods the same energy of the frequency
Reflect the height of flood risk.Therefore choose " rainfall ", " heavy rain frequency " and " frequency is flooded in historical flood " 3 indexs.
Pregnant calamity environment, features of terrain and basin water system and the relationship of flood danger level are the closest, wherein features of terrain master
" absolute elevation " and " terrain slope " 2 indexs are shown as, and basin water system and the relationship of flood danger level are mainly shown as
The rank of water system itself.River, lake and flood storage and detention basin surrounding area, except flood caused by by local precipitation threaten in addition to, also by
Pass by flood because it is lower seep, the flood of unrestrained dike and the formation such as inrush threatens, this biggish area of Partial Flood degree of danger is referred to as slow
Area is rushed, and by " buffer level " as another evaluation index from the aspect of pregnant calamity environment.
Hazard-affected body, emphasis consider its vulnerability and ability of preventing and reducing natural disasters, and choose " density of population ", " GDP per capita " and " flood control
3 indexs of standard ".Under the premise of not considering to prevent and reduce natural disasters level difference, the Levels of Social Economic Development in region is in certain journey
On degree can image study area flood struck potential loss degree, and " flood control capacity " can be used as ability integration of preventing and reducing natural disasters and comment
Valence index.
Table 1
Step 2 is standardized the data in step 1, in data normalization processing, refers to for negative correlation
Mark seeks its inverse, realizes its conversion between positive correlation index.Storm rainfall, historical flood flood the frequency, the density of population, people
Equal GDP and flood danger level are positive correlation, absolute elevation, terrain slope, basin water system buffer level, flood control capacity with
Flood danger level becomes negative correlativing relation.The standardization formula of flood danger level achievement data is as follows:
In formula, xiFor each grid point initial data series of certain index, xmaxAnd xminMaximum and minimum value respectively therein；
xi' it is to standardize later value, between 0 to 100.
Step 3 determines regional flood Hazard Risk Assessment index weights with FAHP Fuzzy AHP, and detailed process is such as
Shown in Fig. 2.
After step 31, recursive hierarchy structure are established, the membership of element has been determined between upper and lower level, extracts flood
Hazard Risk Assessment index；
Step 32 assumes that above one layer of element is criterion, and the next layer of element dominated is u1, u2..., un, using two-by-two
The method compared assigns corresponding weight for the relative importance of upper one layer of element by them, in this way, n is a by comparison element
Construct multilevel iudge matrix two-by-two:
A=(aij)n×n (12)
Wherein, uiWith ujIndicate the lower layer's Index element being compared to each other, aijIndicate element uiWith ujRelative to upper one layer of element
Importance proportion quotiety；
Step 33, according to proportion quotiety conversion formula, convert Fuzzy Complementary Judgment Matrices B for judgment matrixn×n, expression
Formula is as follows:
bij=logαaij+0.5 (13)
Bn×n=(bij)n×n (14)
In formula, bijFor uiWith ujRelative to the transition proportion quotiety of upper layer element,
bijWhen=0.5, uiWith ujImportance is identical；
bijWhen > 0.5, uiCompare ujIt is important；
bijWhen < 0.5, ujCompare uiIt is important, and b need to be metii=0.5, bij+bji=1 α >=81 >=81；
Fuzzy consistent judgment matrix R is converted by Fuzzy Complementary Judgment Matricesn×n,
Rn×n=(rij)n×n (17)
Step 34 asks respective weights vector to calculate each layer evaluation index that is, according to the judgment matrix of consistency check qualification
For the relative weighting w of flood danger level (decision objective)1, w2..., wn, relative weighting can be write as vector form, i.e. and ω=
(W1,W2,...,Wn)T；
Step 35, by fuzzy consistent judgment matrix Rn×nIt is normalized, and uses eigenvalue method, i.e. formula (18),
Find out maximum eigenvalue λmax；
Rn×nω=λmaxω (18)
Step 35 carries out consistency check, that is, passes through maximum eigenvalue λmaxCalculate coincident indicator CI:
Work as CI=0, it is completely the same；CI value is smaller, and judgment matrix is more close to safe consistent degree.
To prevent judgment matrix from deviateing safe consistent degree, Aver-age Random Consistency Index RI is introduced, and take CR to measure
The index of matrix consistency.
When CR≤0.1, then it is assumed that the consistency of fuzzy consistent judgment matrix can receive, and be otherwise considered as fuzzy consensus and sentence
Disconnected matrix is unsatisfactory for consistency, needs to be adjusted judgment matrix, until consistency check reaches requirement；
Step 4 establishes flood Hazard degree assessment model and draws flood risk mapping.It is constructed according to Fuzzy AHP comprehensive
Close evaluation model the way of thinking, according to flood cause disaster system composition, establish the flood Hazard degree assessment index of Megapolis
System, the basic unit using 100m × 100m grid as flood risk assessment, using GIS spatial data analytic function to each
A index carries out data rasterizing and standardization, constructs judgment matrix corresponding with index system, calculates all kinds of indexs
Relative weighting and different grid cell flood danger levels, are achieved in the Megapolis flood based on Fuzzy AHP
The foundation of Hazard degree assessment model and the drafting of flood risk mapping.And according to " flood risk zoning fire protection technology ", flood is endangered
Dangerous degree is divided into 5 ranks, i.e., extremely low dangerous, low dangerous, middle danger, high-risk, high danger.
Step 5 further carries out flood risk diagnosis using flood risk mapping establishment achievement, from distribution of rainfall feature, goes through
History big flood characteristic distributions, each urban storm ponding risk distribution feature angularly, all-dimensional multi-angle ground system diagnostics group of cities
The flood risk distribution characteristics in region.
Claims (1)
1. a kind of city different underlying surface mitigation effect analysis method, which is characterized in that this method includes below scheme:
Step 1: building flood risk assessment index system, constructed assessment indicator system are not destination layer, rule layer and refer to
3 level of layer are marked, wherein destination layer is flood danger level simple target；Rule layer includes Flood inducing factors, pregnant calamity environment, hazard-affected
Body；The setting of indicator layer should be corresponding with rule layer, Flood inducing factors include rainfall, heavy rain frequency and historical flood flood the frequency,
Hazard-affected body includes height above sea level, terrain slope and basin water system buffer area, and hazard-affected body includes the density of population, GDP per capita and water conservancy
Engineering flood control capacity；
Step 2: the data in step 1 are standardized,
It is positive correlation, height above sea level that storm rainfall, historical flood, which flood the frequency, the density of population, GDP per capita and flood danger level,
Journey, terrain slope, basin water system buffer level, flood control capacity and flood danger level become negative correlativing relation；For negative correlation
Index seeks its inverse, realizes its conversion between positive correlation index；The standardization formula of flood danger level achievement data
It is as follows:
In formula, xiFor each grid point initial data series of certain index, xmaxAnd xminMaximum and minimum value respectively therein；xi' be
Later value is standardized, between 0 to 100；
Step 3: determining evaluation criterion weight: the membership by determining element between upper and lower level establishes Recurison order hierarchy knot
Structure determines its relative importance using the method compared two-by-two to each Index element of the identical membership of same level,
Thus Judgement Matricies, and it is translated into fuzzy judgment matrix, respective weights value is obtained, following processing is specifically included:
After step 31, recursive hierarchy structure are established, the membership of element has been determined between upper and lower level, extracts flood damage
Risk Evaluation Factors；
Step 32 assumes that above one layer of element is criterion, and the next layer of element dominated is u1, u2..., un, using comparing two-by-two
Method, corresponding weight is assigned for the relative importance of upper one layer of element by them, in this way, n are constructed by comparison element
Multilevel iudge matrix two-by-two:
A=(aij)n×n (12)
Wherein, uiWith ujIndicate the lower layer's Index element being compared to each other, aijIndicate element uiWith ujWeight relative to upper one layer of element
Want sex ratio scale；
Step 33, according to proportion quotiety conversion formula, convert Fuzzy Complementary Judgment Matrices B for judgment matrixn×n, expression formula is such as
Under:
bij=logαaij+0.5 (13)
Bn×n=(bij)n×n (14)
In formula, bijFor uiWith ujRelative to the transition proportion quotiety of upper layer element,
bijWhen=0.5, uiWith ujImportance is identical；
bijWhen > 0.5, uiCompare ujIt is important；
bijWhen < 0.5, ujCompare uiIt is important, and b need to be metii=0.5, bij+bji=1 α >=81 >=81；
Fuzzy consistent judgment matrix R is converted by Fuzzy Complementary Judgment Matricesn×n,
Rn×n=(rij)n×n (17)
Step 34 seeks respective weights vector, i.e., according to the judgment matrix of consistency check qualification, calculate each layer evaluation index for
The relative weighting w of flood danger level (decision objective)1, w2..., wn, relative weighting can be write as vector form, i.e. ω=(W1,
W2,...,Wn)T；
Step 35, by fuzzy consistent judgment matrix Rn×nIt is normalized, and uses eigenvalue method, i.e. formula (18), find out
Maximum eigenvalue λmax；
Rn×nω=λmaxω (18)
Step 36 carries out consistency check, that is, passes through maximum eigenvalue λmaxCalculate coincident indicator CI:
Work as CI=0, it is completely the same；CI value is smaller, and fuzzy consistent judgment matrix is more close to safe consistent degree；
To prevent judgment matrix from deviateing safe consistent degree, Aver-age Random Consistency Index RI is introduced, and take CR to measure matrix
The index of consistency:
When CR≤0.1, then it is assumed that the consistency of fuzzy consistent judgment matrix can receive, and be otherwise considered as fuzzy consensus and judge square
Battle array is unsatisfactory for consistency, needs to be adjusted judgment matrix, until consistency check reaches requirement；
Step 4: establishing flood Hazard degree assessment model and drawing flood risk mapping；
Step 5: carrying out flood risk diagnosis using flood risk mapping.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111079595A (en) * | 2019-12-04 | 2020-04-28 | 天津大学 | Novel concept and intelligent risk identification method for dynamic flood risk graph |
| CN111724033A (en) * | 2020-05-14 | 2020-09-29 | 天津大学 | Flood risk evaluation and fine zoning method based on random set theory |
| CN111784212A (en) * | 2020-08-06 | 2020-10-16 | 中国水利水电科学研究院 | Flood control risk identification and evaluation method for water diversion project |
| CN111784833A (en) * | 2020-06-23 | 2020-10-16 | 长江空间信息技术工程有限公司(武汉) | WebGL-based flood evolution situation three-dimensional dynamic visualization display method |
| CN112580985A (en) * | 2020-12-22 | 2021-03-30 | 国家海洋环境监测中心 | Coastal erosion risk assessment method |
| CN113496359A (en) * | 2021-07-13 | 2021-10-12 | 天津大学 | Multi-combination subjective and objective uniform optimization empowerment risk evaluation method |
| CN113869354A (en) * | 2021-08-16 | 2021-12-31 | 天津大学 | Comprehensive evaluation method for risk of levee in ice flood season |
| CN115994686A (en) * | 2022-11-21 | 2023-04-21 | 中国科学院地理科学与资源研究所 | Multidimensional flood risk assessment method based on game theory combined weighting |
| CN116187769A (en) * | 2023-05-04 | 2023-05-30 | 四川省安全科学技术研究院 | Urban flood disaster risk studying and judging method based on scene simulation |
| CN117634870A (en) * | 2023-10-23 | 2024-03-01 | 北京市勘察设计研究院有限公司 | Urban road collapse risk zoning method, device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111079595A (en) * | 2019-12-04 | 2020-04-28 | 天津大学 | Novel concept and intelligent risk identification method for dynamic flood risk graph |
| CN111724033A (en) * | 2020-05-14 | 2020-09-29 | 天津大学 | Flood risk evaluation and fine zoning method based on random set theory |
| CN111784833B (en) * | 2020-06-23 | 2023-06-09 | 长江空间信息技术工程有限公司(武汉) | Flood evolution situation three-dimensional dynamic visualization display method based on WebGL |
| CN111784833A (en) * | 2020-06-23 | 2020-10-16 | 长江空间信息技术工程有限公司(武汉) | WebGL-based flood evolution situation three-dimensional dynamic visualization display method |
| CN111784212A (en) * | 2020-08-06 | 2020-10-16 | 中国水利水电科学研究院 | Flood control risk identification and evaluation method for water diversion project |
| CN112580985A (en) * | 2020-12-22 | 2021-03-30 | 国家海洋环境监测中心 | Coastal erosion risk assessment method |
| CN113496359A (en) * | 2021-07-13 | 2021-10-12 | 天津大学 | Multi-combination subjective and objective uniform optimization empowerment risk evaluation method |
| CN113496359B (en) * | 2021-07-13 | 2022-03-22 | 天津大学 | Multi-combination subjective and objective uniform optimization empowerment risk evaluation method |
| CN113869354A (en) * | 2021-08-16 | 2021-12-31 | 天津大学 | Comprehensive evaluation method for risk of levee in ice flood season |
| CN113869354B (en) * | 2021-08-16 | 2024-05-07 | 天津大学 | Comprehensive evaluation method for risk of embankment in flood season |
| CN115994686A (en) * | 2022-11-21 | 2023-04-21 | 中国科学院地理科学与资源研究所 | Multidimensional flood risk assessment method based on game theory combined weighting |
| CN115994686B (en) * | 2022-11-21 | 2023-07-28 | 中国科学院地理科学与资源研究所 | Multidimensional flood risk assessment method based on game theory combined weighting |
| CN116187769A (en) * | 2023-05-04 | 2023-05-30 | 四川省安全科学技术研究院 | Urban flood disaster risk studying and judging method based on scene simulation |
| CN117634870A (en) * | 2023-10-23 | 2024-03-01 | 北京市勘察设计研究院有限公司 | Urban road collapse risk zoning method, device and storage medium |
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Inventor after: Tian Fuchang Inventor after: Yuan Ximin Inventor after: Wang Xiujie Inventor after: Sang Linhao Inventor after: Sun Yu Inventor after: Wang Yanpeng Inventor before: Yuan Ximin Inventor before: Wang Xiujie Inventor before: Tian Fuchang Inventor before: Sang Linhao Inventor before: Sun Yu Inventor before: Wang Yanpeng |
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Application publication date: 20191115 |