CN112785053B - Method and system for forecasting urban drainage basin flood - Google Patents
Method and system for forecasting urban drainage basin flood Download PDFInfo
- Publication number
- CN112785053B CN112785053B CN202110057607.3A CN202110057607A CN112785053B CN 112785053 B CN112785053 B CN 112785053B CN 202110057607 A CN202110057607 A CN 202110057607A CN 112785053 B CN112785053 B CN 112785053B
- Authority
- CN
- China
- Prior art keywords
- rainfall
- flood
- time
- simulation
- urban
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000004088 simulation Methods 0.000 claims abstract description 93
- 230000015654 memory Effects 0.000 claims description 38
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 238000009825 accumulation Methods 0.000 claims description 13
- 238000012417 linear regression Methods 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 230000002411 adverse Effects 0.000 claims description 5
- 238000007621 cluster analysis Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 5
- 238000013461 design Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 241000283014 Dama Species 0.000 description 1
- 238000006424 Flood reaction Methods 0.000 description 1
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- JNSGIVNNHKGGRU-JYRVWZFOSA-N diethoxyphosphinothioyl (2z)-2-(2-amino-1,3-thiazol-4-yl)-2-methoxyiminoacetate Chemical compound CCOP(=S)(OCC)OC(=O)C(=N/OC)\C1=CSC(N)=N1 JNSGIVNNHKGGRU-JYRVWZFOSA-N 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Alarm Systems (AREA)
Abstract
The application discloses a prediction method and a prediction system for urban watershed flooding, wherein the method comprises the following steps: determining the rain-type space-time distribution of the urban area according to the historical data; establishing a flood simulation scene library by simulating parameters under different scenes of the area by utilizing a refined flood model according to the rain-type space-time distribution and the river flood boundary conditions; establishing a rainstorm flood relational expression and a waterlogging simulation relational expression based on the simulation data; correcting the acquired forecast rainfall data according to the rain-type space-time distribution, and dividing different scenes; and performing data matching on the rainstorm flood relation formula and the waterlogging simulation relation formula with different scenes, and taking the simulation data in the flood simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value. The method can rapidly calculate the peak flow and the total flood quantity of each node in the forestation period, and the information of the position of the ponding point, the depth of the ponding, the duration of the ponding and the like, thereby realizing rapid prediction and judgment of urban flood disasters and competing for precious time for emergency management of flood control and drainage.
Description
Technical Field
The application relates to the technical field of hydrologic prediction, in particular to a method and a system for forecasting urban watershed flooding.
Background
Flood disasters are one of natural disasters with the greatest loss to people in the current world, are affected by urban rain island effects, are frequently caused by local short-time heavy rainfall in urban areas, are superimposed by special topography and underlying conditions, and are caused by population, socioeconomic and other disaster factors, so that the risk and uncertainty of urban flood disasters are greatly increased.
The numerical model is an important technical means of urban flood control and disaster reduction, as the urban flood model is becoming finer, simulation elements and parameters are becoming more and more abundant, model development and monitoring information are highly mismatched, the redundancy of a model algorithm is continuously increased, and the problems that the time required for forecasting, simulating and calculating urban flood is longer and the time reserved for flood control command decision is short exist due to the natural attribute of large urban flood yield and rapid convergence are existed.
Disclosure of Invention
Therefore, the method and the system for forecasting the urban drainage basin flood provided by the application overcome the defects that the time required for forecasting, simulating and calculating the urban drainage basin flood is long and the time reserved for flood prevention command decision is short in the prior art.
In order to achieve the above purpose, the present application provides the following technical solutions:
in a first aspect, an embodiment of the present application provides a method for predicting urban watershed flooding, including:
extracting a historical scene rainfall data set of an urban area to be predicted, analyzing rainfall time sequence samples of each rainfall station of each scene within preset time, and determining the rainfall time-space distribution of the urban area;
according to the rain-type space-time distribution and river flood boundary conditions, using a refined flood model to simulate flood characteristic parameters and ponding characteristic parameters of each river channel node under different conditions in the area, and establishing a flood simulation scene library;
establishing a rainstorm flood relational expression and a waterlogging simulation relational expression based on simulation data of the flood simulation scene library;
according to the space-time distribution of the rain, correcting the rainfall data obtained in the rainfall forecasting process, and dividing the safety scene and the unfavorable scene; and respectively carrying out data matching on the storm flood relation and the waterlogging simulation relation with the safety scene and the unfavorable scene, and taking corresponding simulation data in the flood simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value.
In one embodiment, the step of determining a rain-type spatiotemporal distribution of the urban area comprises:
according to rainfall time sequence samples of each rainfall station in the preset time of each occasion in the urban area to be predicted, calculating rainfall proportion of each rainfall station in the preset time of each occasion;
dividing weather movement paths of all the scenes of the region based on a k-means algorithm; carrying out cluster analysis on the weather moving path and the rainfall proportion by using a k-means algorithm, and classifying and extracting rainfall rain types of each rainfall station;
constructing Thiessen polygons according to the positions of the rainfall stations, and dividing the rainfall station rainfall space distribution; and determining the rainfall type space-time distribution of the area based on the rainfall type of each rainfall station and the corresponding rainfall type space distribution.
In one embodiment, the rainfall ratio of each session within the preset time is calculated by the following formula:
x i ＝max{P i j },j∈[1,n]
wherein m is the number of rainfall stations; n is the total number of rainfall occasions; t is a rainfall period; x is x i Presetting a time rainfall time sequence for an ith rainfall station; p (P) i j The total amount of rainfall at a preset time for the jth rainfall at the ith rainfall station,the rainfall station is used for determining the rainfall of the ith rainfall station in the ith period in a rainfall sequence at preset time; />Presetting a time rainfall total amount for an ith rainfall station; />And the rainfall proportion of the t period in the rainfall sequence of the preset time of the ith rainfall station is set.
In one embodiment, the predicted rainfall process is used as a safety scenario, and the predicted rainfall process after the actual rain form correction is used as a disadvantageous scenario.
In an embodiment, applying a refined flood model to simulate flood characteristic parameters and ponding characteristic parameters of each river channel node under different conditions in the area, and establishing a flood simulation scene library, including:
according to the design rain type and rainfall rain type, distinguishing long duration scenes from short duration scenes, obtaining river channel flood boundary conditions with frequencies corresponding to the long duration scenes and the short duration scenes, applying a refined flood model to simulate, generating flood peak flow and total flood quantity, regional water accumulation point positions, water accumulation depth and water accumulation duration parameters of each river channel rainfall station, and constructing a flood simulation scene library.
In one embodiment, the rainfall data from the forecast rainfall process is corrected by the following formula:
in the formula ,the rainfall corrected for the t period of the i-th area; p (P) i The total quantity of rainfall forecast for the ith area; />The ratio of rainfall in the ith period is the ith region.
In an embodiment, based on simulation data of a flood simulation scenario library, establishing a storm flood relation and a waterlogging simulation relation includes: establishing a linear regression mathematical expression according to the linear relation among the total flood amount, the peak flow and the peak time of different scenes, and taking the linear regression mathematical expression as a storm flood relation; and establishing a linear regression mathematical expression according to the linear relation among the ponding depth, the ponding duration and the ponding range of different scenes, and taking the linear regression mathematical expression as a waterlogging simulation relation.
In a second aspect, an embodiment of the present application provides a prediction system for urban watershed flooding, including:
the rainfall time sequence analysis module is used for analyzing rainfall time sequence samples of each rainfall station in preset time of each scene to determine the rainfall time-space distribution of the urban area;
the flood simulation scene library building module is used for building a flood simulation scene library by simulating flood characteristic parameters and ponding characteristic parameters of each river channel node in different scenes of the area by utilizing a refined flood model according to the rain-type space-time distribution and river channel flood boundary conditions;
the flood and waterlogging relation establishing module is used for establishing a rainstorm flood relation and waterlogging simulation relation based on the simulation data of the flood simulation scene library;
the urban river basin flood information forecasting module is used for correcting rainfall data obtained in a rainfall forecasting process according to the rainfall type space-time distribution and dividing a safety scene and an adverse scene; and respectively carrying out data matching on the storm flood relation and the waterlogging simulation relation with the safety scene and the unfavorable scene, and taking corresponding simulation data in the flood simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value.
In a third aspect, an embodiment of the present application provides a terminal, including: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to execute the urban river basin flood prediction method according to the first aspect of the embodiment of the application.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where computer instructions are stored, where the computer instructions are configured to cause the computer to execute the method for predicting urban river basin flooding according to the first aspect of the embodiment of the present application.
The technical scheme of the application has the following advantages:
the prediction method and the prediction system for urban drainage basin flooding provided by the application determine the rain-type space-time distribution of urban areas according to historical data; establishing a flood simulation scene library by simulating parameters under different scenes of the area by utilizing a refined flood model according to the rain-type space-time distribution and the river flood boundary conditions; establishing a rainstorm flood relational expression and a waterlogging simulation relational expression based on the simulation data; correcting the acquired forecast rainfall data according to the rain-type space-time distribution, and dividing different scenes; and performing data matching on the rainstorm flood relation formula and the waterlogging simulation relation formula with different scenes, and taking the simulation data in the flood simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value. The method can rapidly calculate the peak flow and the total flood quantity of each node in the forestation period, and the information of the position of the ponding point, the depth of the ponding, the duration of the ponding and the like, thereby realizing rapid prediction and judgment of urban flood disasters and competing for precious time for emergency management of flood control and drainage.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a specific example of a method for predicting urban watershed flooding according to an embodiment of the present application;
fig. 2 is a diagram of 6 types of rain types in beijing city according to an embodiment of the present application;
fig. 3 is a diagram showing 6 rain-type distribution diagrams in beijing city according to an embodiment of the present application;
FIG. 4 is a graph showing the relationship between the rainfall capacity of a Zhang Guwan gate and the peak flow rate according to an embodiment of the present application;
fig. 5 is a block diagram of a prediction system for urban watershed flooding according to an embodiment of the present application;
fig. 6 is a composition diagram of a specific example of a terminal according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the application are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the technical features of the different embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
Example 1
The method for predicting urban river basin flooding provided by the embodiment of the application, as shown in fig. 1, comprises the following steps:
step S1: and extracting a historical scene rainfall data set of the urban area to be predicted, analyzing rainfall time sequence samples of each rainfall station in preset time of each scene, and determining the rainfall time-space distribution of the urban area.
In the embodiment of the application, according to the historical site rainfall data set of the urban area to be predicted, the maximum 24-hour rainfall time sequence of each site is extracted, the proportion of the rainfall in each time period to the total rainfall of the maximum 24 hours is calculated, and the method is not limited by the example, and the selection of the corresponding event segments is carried out according to the actual requirements in the actual application.
In an embodiment of the present application, the step of determining a rain-type spatiotemporal distribution of the urban area includes: according to rainfall time sequence samples of each rainfall station in the preset time of each occasion in the urban area to be predicted, calculating rainfall proportion of each rainfall station in the preset time of each occasion; dividing weather movement paths of all the scenes of the region based on a k-means algorithm; carrying out cluster analysis on the weather moving path and the rainfall proportion by using a k-means algorithm, and classifying and extracting rainfall rain types of each rainfall station; constructing Thiessen polygons according to the positions of the rainfall stations, and dividing the rainfall station rainfall space distribution; and determining the rain type space-time distribution of the region based on the rainfall type of each rainfall station and the corresponding field time rain type space distribution.
In the embodiment of the application, the rainfall proportion of each occasion in the preset time is calculated by the following formula:
x i ＝max{P i j },j∈[1,n]
wherein m is the number of rainfall stations; n is the total number of rainfall occasions; t is a rainfall period; x is x i Presetting a time rainfall time sequence for an ith rainfall station; p (P) i j The total amount of rainfall at a preset time for the jth rainfall at the ith rainfall station,the rainfall station is used for determining the rainfall of the ith rainfall station in the ith period in a rainfall sequence at preset time; />Presetting a time rainfall total amount for an ith rainfall station; />And the rainfall proportion of the t period in the rainfall sequence of the preset time of the ith rainfall station is set.
Step S2: according to the rain-type space-time distribution and river flood boundary conditions, a refined flood model is utilized to simulate flood characteristic parameters and ponding characteristic parameters of each river channel node under different conditions in the area, and a flood simulation scene library is established.
In the embodiment of the application, a refined flood model is applied to simulate flood characteristic parameters and ponding characteristic parameters of each river channel node under different conditions in the area, and a flood simulation scene library is established, which comprises the following steps: according to the design rain type and rainfall rain type, distinguishing long duration scenes from short duration scenes, obtaining river channel flood boundary conditions with frequencies corresponding to the long duration scenes and the short duration scenes, applying a refined flood model to simulate, generating flood peak flow and total flood quantity, regional water accumulation point positions, water accumulation depth and water accumulation duration parameters of each river channel rainfall station, and constructing a flood simulation scene library.
In one embodiment, according to the design of the rain pattern and the rainfall pattern, the long duration and the short duration are distinguished, the long duration is 1 year, 3 years, 5 years, 10 years, 20 years, 50 years and 100 years, the short duration is 1 year, 3 years, 5 years and 10 years, which is only by way of example, but not by way of limitation, and the corresponding long duration and short duration are selected according to the actual requirements in practical application; inputting boundary conditions of river channel floods with corresponding frequencies, for example, performing rainfall distribution through Chicago rainfall patterns, performing scene setting on the storm space distribution according to the similar rainfall pattern distribution rule of the research area, and setting corresponding boundary conditions according to actual requirements in practical application by way of example only and not limitation; and simulating by applying a refined flood model, and simulating and outputting information such as flood peak flow and total flood of each river hydrologic site, regional water accumulation points, water accumulation depth, water accumulation duration and the like, so as to construct a flood simulation scene library.
Step S3: and establishing a rainstorm flood relational expression and a waterlogging simulation relational expression based on the simulation data of the flood simulation scene library.
In the embodiment of the application, a linear regression mathematical expression is established according to the linear relations of the total flood quantity, the peak flood quantity and the peak time of different scenes to be used as a storm flood relation; and establishing a linear regression mathematical expression according to the linear relation among the ponding depth, the ponding duration and the ponding range of different scenes, and taking the linear regression mathematical expression as a waterlogging simulation relation.
Step S4: according to the space-time distribution of the rain, correcting the rainfall data obtained in the rainfall forecasting process, and dividing the safety scene and the unfavorable scene; and respectively carrying out data matching on the storm flood relation and the waterlogging simulation relation with the safety scene and the unfavorable scene, and taking corresponding simulation data in the flood simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value.
In the embodiment of the application, the rainfall forecasting process is used as a safety scene, and the rainfall forecasting process after the actual rain correction is used as a disadvantageous scene.
In a specific embodiment, the subjective prediction climate mode is basic data of flood risk early warning and flood scheduling, the grid with the spatial resolution of 1km, the time resolution of 1h and the future 24h rainfall process in the prediction period, products are predicted based on the subjective prediction climate mode, and the coverage range of 24h rainfall exceeding one year (the rainfall is more than 47 mm) is extracted; correcting the rainfall forecasting process by using the extracted rain pattern to serve as the most unfavorable scene, and taking the rainfall forecasting process as the most safe scene; and respectively calculating the flood peak flow and the total flood amount of each river channel node of the two scenes according to rainfall characteristic parameters in combination with a quantitative expression of the storm flood and a simulation scene library, and calling the most similar waterlogging simulation scene to rapidly predict the waterlogging.
In the embodiment of the application, the rainfall data of the rainfall forecast process is corrected by the following formula:
in the formula ,the rainfall corrected for the t period of the i-th area; p (P) i The total quantity of rainfall forecast for the ith area; />The ratio of rainfall in the ith period is the ith region.
In a specific embodiment, the river basin of Beijing city cold water is taken as the research area, and the area of the river basin is 655km 2 Taking rainfall as an example, 8 months in 2020, 12 days:
first, according to 276 times total strong convection weather movement path data of Beijing cities, 6 main movement paths are extracted, and k-means algorithm parameters k are determined to be 6 types based on the 6 main movement paths. (1) Northwest road 1: the source is positioned in the dam head area at the junction of the valley and the Mongolia plateau. Moving along the ocean river and the eternal river valley to the southeast near Zhang Bei, wan quan and Zhang Jiang kou; (2) northwest road 2: starting from the western section of the Dama mountain 150km from the northwest of Beijing, moving to the south via the red city, sea lump mountain, yanqing and Changping; (3) west way: is generated in mountain areas such as Beijing western Zhaitang and Ling Cheng, and is transmitted to east or northeast; (4) north way: the main points are generated in the eastern section of the large-horse mountain, and are passed through the areas of dam heads, yanqing, army Dou Shan, changping, cis-sense and the like; (5) east road: moving from north to south; (6) locally generating: only 6%. Aiming at historical rainfall data, extracting a maximum 24-hour rainfall sequence of each station, and calculating the proportion of the rainfall in each period to the total rainfall of the maximum 24 hours; using the result as a historical storm data set, and applying a k-means algorithm to perform cluster analysis to identify the rain type of each rainfall site; as shown in fig. 2, 6 types of rain types are identified. Based on the position of the rainfall station in the river basin, a Thiessen polygon is constructed, and the rainfall spatial distribution is divided, as shown in figure 3, and is 6 rainfall distribution conditions.
The method comprises the following specific steps of: long duration 24h rainfall scenario set: and according to the Beijing hydrological handbook-storm chart set, inquiring the maximum rainfall total of 1h, 6h and 24h in different reproduction periods within the range of a research area, and calculating the 24h design rainfall process. Short duration 1h rainfall scenario set: and calculating and designing rainfall by using a storm intensity formula, and carrying out rainfall process distribution based on the Chicago rainfall pattern. According to the distribution rule of the storm in the cold water river basin, setting the space distribution scene of the storm according to the upstream, midstream or downstream of the center of the full-basin drop storm. Based on the refined flood model, setting and designing rainfall scenes, and simulating flood peak flow and total flood of each river channel node, and regional ponding depth, total ponding and ponding duration characteristic parameters. And (5) integrating the information and establishing a flood simulation scene library.
The quantitative relation of the storm flood is established, and the specific steps are as follows: and (3) statistically analyzing the linear relation between the rainfall total amount and the flood peak flow amount, and establishing a linear regression mathematical expression. As shown in fig. 4, taking a bay gate as an example, the total rainfall of the Zhang Guwan gate has a better linear relationship with the peak flow, the correlation coefficient reaches 0.99, and the rainfall is fitted with the peak flow: y= -0.0043x2+5.48x-27.34.
The method for forecasting the storm flood comprises the following specific steps: forecasting products based on subjective forecasting climate mode, and extracting the non-rainfall according to the total rainfall forecastThe rainfall from 24 hours exceeds one year (rainfall)>47 mm/h) of the rainfall landing zone; multiplying the total quantity of the forecast rainfall by the rain type proportion of each unit to obtain a modified forecast rainfall process; and taking the forecast rainfall process as the safest scene, correcting the forecast rainfall process by using the actual rainfall as the worst scene, and calculating the flood peak flow, the total flood and the flood duration of each river channel node according to the rainfall characteristic parameter and the quantitative expression of the storm flood. The results show that: according to the quantitative relation of the established storm flood, calculating the peak flow of the open bay gate to be 216m 3 /s, measured flow rate 211m 3 The relative error is 2 percent, and the simulation effect is good; and according to the calculation result, invoking the most similar waterlogging simulation scene, and finding 69 simulation waterlogging water accumulation points in the simulation scene in the matching scene library. And according to the result, releasing flood forecast information.
According to the prediction method for urban river basin flooding, which is provided by the embodiment of the application, the rain-type space-time distribution of the urban area is determined according to the historical data; establishing a flood simulation scene library by simulating parameters under different scenes of the area by utilizing a refined flood model according to the rain-type space-time distribution and the river flood boundary conditions; establishing a rainstorm flood relational expression and a waterlogging simulation relational expression based on the simulation data; correcting the acquired forecast rainfall data according to the rain-type space-time distribution, and dividing different scenes; and performing data matching on the rainstorm flood relation formula and the waterlogging simulation relation formula with different scenes, and taking the simulation data in the flood simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value. The method can rapidly calculate the peak flow and the total flood quantity of each node in the forestation period, and the information of the position of the ponding point, the depth of the ponding, the duration of the ponding and the like, thereby realizing rapid prediction and judgment of urban flood disasters and competing for precious time for emergency management of flood control and drainage.
Example 2
An embodiment of the present application provides a prediction system for urban watershed flooding, as shown in fig. 5, including:
the rainfall time-space distribution determining module 1 is used for extracting a historical scene rainfall data set of an urban area to be predicted, analyzing rainfall time sequence samples of each rainfall station in preset time of each scene, and determining rainfall time-space distribution of the urban area; this module performs the method described in step S1 in embodiment 1, and will not be described here again.
The flood simulation scene library building module 2 is used for building a flood simulation scene library by simulating flood characteristic parameters and ponding characteristic parameters of each river channel node in different scenes of the area by utilizing a refined flood model according to the rain-type space-time distribution and river channel flood boundary conditions; this module performs the method described in step S2 in embodiment 1, and will not be described here.
The flood and waterlogging relation establishing module 3 is used for establishing a storm flood relation and a waterlogging simulation relation based on the simulation data of the flood simulation scene library; this module performs the method described in step S3 in embodiment 1, and will not be described here.
The urban river basin flood information forecasting module 4 is used for correcting rainfall data obtained in a rainfall forecasting process according to the rainfall type space-time distribution and dividing a safety scene and an unfavorable scene; respectively carrying out data matching on the storm flood relation and the waterlogging simulation relation with the safety scene and the unfavorable scene, and taking corresponding simulation data in the waterlogging simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value; this module performs the method described in step S4 in embodiment 1, and will not be described here.
The embodiment of the application provides a prediction system for urban watershed flooding, which determines the rain-type space-time distribution of urban areas according to historical data; establishing a flood simulation scene library by simulating parameters under different scenes of the area by utilizing a refined flood model according to the rain-type space-time distribution and the river flood boundary conditions; establishing a rainstorm flood relational expression and a waterlogging simulation relational expression based on the simulation data; correcting the acquired forecast rainfall data according to the rain-type space-time distribution, and dividing different scenes; and performing data matching on the rainstorm flood relation formula and the waterlogging simulation relation formula with different scenes, and taking the simulation data in the flood simulation scene library as forecast urban river basin flood information when the matching degree is larger than a preset value. The method can rapidly calculate the peak flow and the total flood quantity of each node in the forestation period, and the information of the position of the ponding point, the depth of the ponding, the duration of the ponding and the like, thereby realizing rapid prediction and judgment of urban flood disasters and competing for precious time for emergency management of flood control and drainage.
Example 3
An embodiment of the present application provides a terminal, as shown in fig. 6, including: at least one processor 401, such as a CPU (Central Processing Unit ), at least one communication interface 403, a memory 404, at least one communication bus 402. Wherein communication bus 402 is used to enable connected communications between these components. The communication interface 403 may include a Display screen (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may further include a standard wired interface and a wireless interface. The memory 404 may be a high-speed RAM memory (Random Access Memory) or a nonvolatile memory (nonvolatile memory), such as at least one magnetic disk memory. The memory 404 may also optionally be at least one storage device located remotely from the aforementioned processor 401. Wherein the processor 401 may perform the method of predicting urban watershed flooding in embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for executing the prediction method of urban watershed flooding in embodiment 1. The communication bus 402 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. Communication bus 402 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in fig. 6, but not only one bus or one type of bus. Wherein the memory 404 may include volatile memory (English) such as random-access memory (RAM); the memory may also include a nonvolatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated as HDD) or a solid-state drive (english: SSD); memory 404 may also include a combination of the above types of memory. The processor 401 may be a central processor (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP.
Wherein the memory 404 may include volatile memory (English) such as random-access memory (RAM); the memory may also include a nonvolatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated as HDD) or a solid state disk (english: solid-state drive, abbreviated as SSD); memory 404 may also include a combination of the above types of memory.
The processor 401 may be a central processor (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP.
Wherein the processor 401 may further comprise a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof (English: programmable logic device). The PLD may be a complex programmable logic device (English: complex programmable logic device, abbreviated: CPLD), a field programmable gate array (English: field-programmable gate array, abbreviated: FPGA), a general-purpose array logic (English: generic array logic, abbreviated: GAL), or any combination thereof.
Optionally, the memory 404 is also used for storing program instructions. The processor 401 may invoke program instructions to implement the prediction method of urban watershed flooding as in embodiment 1 of the present application.
The embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium stores computer executable instructions, wherein the computer executable instructions can execute the urban river basin flood prediction method in the embodiment 1. Wherein the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present application.
Claims (6)
1. A method for predicting urban watershed flooding, comprising:
extracting a historical scene rainfall data set of an urban area to be predicted, analyzing rainfall time sequence samples of each rainfall station of each scene within preset time, and determining rainfall time-space distribution of the urban area, wherein the method comprises the following steps:
according to rainfall time sequence samples of each rainfall station in preset time of each occasion in the urban area to be predicted, calculating the rainfall proportion of each rainfall station in the preset time of each occasion, and calculating the rainfall proportion of each occasion in the preset time by the following formula:
wherein ,the quantity of the rainfall stations; />Is the total number of rainfall occasions; />Is a rainfall period; />Is->Presetting a time rainfall time sequence for each rainfall station; />Is->The>Total amount of rainfall for preset time of field rainfall, +.>Is->The rainfall station is in the rainfall sequence at the preset time +.>Rainfall in each time period; />Is->The rainfall stations preset the total rainfall amount for time; />Is the first/>The first person in a predetermined time rainfall sequence of a rainfall station>Rainfall ratio in each period;
dividing weather movement paths of all the scenes of the region based on a k-means algorithm; carrying out cluster analysis on the weather moving path and the rainfall proportion by using a k-means algorithm, and classifying and extracting rainfall rain types of each rainfall station;
constructing Thiessen polygons according to the positions of the rainfall stations, and dividing the rainfall station rainfall space distribution; determining the rainfall type space-time distribution of the area based on the rainfall type and the rainfall type space distribution of each rainfall station;
according to the rain-type space-time distribution and river flood boundary conditions, using a refined flood model to simulate flood characteristic parameters and ponding characteristic parameters of each river channel node under different conditions in the area, and establishing a flood simulation scene library;
establishing a rainstorm flood relational expression and a waterlogging simulation relational expression based on simulation data of the flood simulation scene library;
according to the space-time distribution of the rain, correcting the rainfall data obtained in the rainfall forecasting process, and dividing the safety situation and the adverse situation, wherein the rainfall forecasting process is used as the safety situation, and the rainfall forecasting process after the actual rain is corrected is used as the adverse situation; and respectively carrying out data matching on the storm flood relation and the waterlogging simulation relation with the safety scene and the unfavorable scene, and when the matching degree is larger than a preset value, taking corresponding simulation data in a flood simulation scene library as forecast urban river basin flood information, wherein rainfall data in a forecast rainfall process are corrected through the following formula:
in the formula ,is->No. 5 of the individual region>Rainfall after time period correction; />Is->The total quantity of forecast rainfall of each area; />Is->No. 5 of the individual region>Period rainfall ratio.
2. The method for predicting urban river basin flooding according to claim 1, wherein the step of applying a refined flooding model to simulate flood characteristic parameters and ponding characteristic parameters of each river channel node in different conditions of the area and establishing a flooding simulation scene library comprises the following steps:
according to the design rain type and rainfall rain type, distinguishing long duration scenes from short duration scenes, obtaining river channel flood boundary conditions with frequencies corresponding to the long duration scenes and the short duration scenes, applying a refined flood model to simulate, generating flood peak flow and total flood quantity, regional water accumulation point positions, water accumulation depth and water accumulation duration parameters of each river channel rainfall station, and constructing a flood simulation scene library.
3. The method for predicting urban river basin flooding according to claim 1, wherein establishing a storm flood relation and a waterlogging simulation relation based on the simulation data of the flood simulation scenario library comprises: establishing a linear regression mathematical expression according to the linear relation among the total flood amount, the peak flow and the peak time of different scenes, and taking the linear regression mathematical expression as a storm flood relation; and establishing a linear regression mathematical expression according to the linear relation among the ponding depth, the ponding duration and the ponding range of different scenes, and taking the linear regression mathematical expression as a waterlogging simulation relation.
4. A prediction system for urban watershed flooding, comprising:
the rainfall time sequence sample of each rainfall station in the preset time is analyzed to determine the rainfall time-space distribution of the urban area, which comprises the following steps:
according to rainfall time sequence samples of each rainfall station in preset time of each occasion in the urban area to be predicted, calculating the rainfall proportion of each rainfall station in the preset time of each occasion, and calculating the rainfall proportion of each occasion in the preset time by the following formula:
wherein ,the quantity of the rainfall stations; />Is the total number of rainfall occasions; />Is a rainfall period; />Is->Presetting a time rainfall time sequence for each rainfall station; />Is->The>Total amount of rainfall for preset time of field rainfall, +.>Is->The rainfall station is in the rainfall sequence at the preset time +.>Rainfall in each time period; />Is->The rainfall stations preset the total rainfall amount for time; />Is->The first person in a predetermined time rainfall sequence of a rainfall station>Rainfall ratio in each period;
dividing weather movement paths of all the scenes of the region based on a k-means algorithm; carrying out cluster analysis on the weather moving path and the rainfall proportion by using a k-means algorithm, and classifying and extracting rainfall rain types of each rainfall station;
constructing Thiessen polygons according to the positions of the rainfall stations, and dividing the rainfall station rainfall space distribution; determining the rainfall type space-time distribution of the area based on the rainfall type and the rainfall type space distribution of each rainfall station;
the flood simulation scene library building module is used for building a flood simulation scene library by simulating flood characteristic parameters and ponding characteristic parameters of each river channel node in different scenes of the area by utilizing a refined flood model according to the rain-type space-time distribution and river channel flood boundary conditions;
the flood and waterlogging relation establishing module is used for establishing a rainstorm flood relation and waterlogging simulation relation based on the simulation data of the flood simulation scene library;
the urban river basin flood information forecasting module is used for correcting rainfall data obtained in a rainfall forecasting process according to the rainfall type space-time distribution, and dividing the rainfall data into a safety scenario and an adverse scenario, wherein the rainfall forecasting process is used as the safety scenario, and the actual rainfall forecasting process after the rainfall type correction is used as the adverse scenario; and respectively carrying out data matching on the storm flood relation and the waterlogging simulation relation with the safety scene and the unfavorable scene, and when the matching degree is larger than a preset value, taking corresponding simulation data in a flood simulation scene library as forecast urban river basin flood information, wherein rainfall data in a forecast rainfall process are corrected through the following formula:
in the formula ,is->No. 5 of the individual region>Rainfall after time period correction; />Is->The total quantity of forecast rainfall of each area; />Is->No. 5 of the individual region>Period rainfall ratio.
5. A terminal, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the urban watershed flood prediction method of any one of claims 1-3.
6. A computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of predicting urban watershed flooding of any one of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110057607.3A CN112785053B (en) | 2021-01-15 | 2021-01-15 | Method and system for forecasting urban drainage basin flood |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110057607.3A CN112785053B (en) | 2021-01-15 | 2021-01-15 | Method and system for forecasting urban drainage basin flood |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112785053A CN112785053A (en) | 2021-05-11 |
| CN112785053B true CN112785053B (en) | 2023-11-03 |
Family
ID=75756616
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110057607.3A Active CN112785053B (en) | 2021-01-15 | 2021-01-15 | Method and system for forecasting urban drainage basin flood |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112785053B (en) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344291B (en) * | 2021-06-29 | 2022-04-15 | 珠江水利委员会珠江水利科学研究院 | Urban inland inundation range forecasting method, device, medium and equipment |
| CN113408211B (en) * | 2021-07-12 | 2022-02-11 | 中国水利水电科学研究院 | Urban inland inundation water accumulation point water accumulation process prediction method and system based on machine learning |
| CN113821939B (en) * | 2021-11-22 | 2022-03-15 | 中机国际工程设计研究院有限责任公司 | Urban rainstorm intensity calculation method, system, equipment and storage medium |
| CN114970315B (en) * | 2022-04-19 | 2023-08-04 | 河海大学 | Urban ponding simulation and rapid prediction method based on space dynamic characteristic deep learning |
| CN115204269B (en) * | 2022-06-15 | 2024-03-12 | 南通市测绘院有限公司 | Urban treatment data fusion method and system based on space-time reference |
| CN115017665B (en) * | 2022-06-29 | 2023-04-04 | 珠江水利委员会珠江水利科学研究院 | Simulation method, device, equipment and storage medium for urban rainstorm flood defense |
| CN114997534B (en) * | 2022-07-29 | 2022-10-21 | 长江水利委员会水文局 | Similar rainfall forecasting method and equipment based on visual features |
| CN115293469B (en) * | 2022-10-10 | 2022-12-27 | 山脉科技股份有限公司 | Urban flood control and drainage risk prediction method |
| CN115358354A (en) * | 2022-10-24 | 2022-11-18 | 中国水利水电科学研究院 | Rainfall space data restoration and reconstruction method |
| CN116049963B (en) * | 2022-12-12 | 2024-01-23 | 中国水利水电科学研究院 | Quick simulation method based on urban oversized flood underground space regulation |
| CN116468269B (en) * | 2023-04-10 | 2023-10-20 | 广州市城市规划勘测设计研究院 | Flood high-risk area identification method, device, equipment and storage medium |
| CN116362419B (en) * | 2023-05-31 | 2023-08-04 | 聊城市科慧市政工程设计院有限公司 | Urban flood control early warning system and method |
| CN117010726B (en) * | 2023-09-29 | 2023-12-08 | 水利部交通运输部国家能源局南京水利科学研究院 | Intelligent early warning method and system for urban flood control |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009300311A (en) * | 2008-06-16 | 2009-12-24 | Toshiba Corp | Flood prediction support device and method for supporting prediction of flood |
| KR20100131902A (en) * | 2009-06-08 | 2010-12-16 | 부경대학교 산학협력단 | Flood prediction information system of high-resolution rapid-updated-coupling and flood prediction information method of high- resolution rapid-updated-coupling |
| RU2548920C1 (en) * | 2013-10-18 | 2015-04-20 | Республика Саха (Якутия), от имени которой выступает уполномоченное Государственным комитетом Республики Саха (Якутия) по инновационной политике и науке лицо - Государственное бюджетное учреждение "Академия наук Республики Саха (Якутия)" (ГБУ АН РС(Я)) | Method of predicting hydrologic environment on obstruction-hazard parts of river |
| CN107220496A (en) * | 2017-05-26 | 2017-09-29 | 上海市气象灾害防御技术中心 | A kind of urban rainstorm waterlogging assesses modeling method |
| CN108984823A (en) * | 2018-05-31 | 2018-12-11 | 北京市水科学技术研究院 | A kind of determination method of combined sewer overflow storage pond scale |
| JP2019148058A (en) * | 2018-02-26 | 2019-09-05 | 三菱電機株式会社 | Flooding prediction evaluation device |
| CN110633865A (en) * | 2019-09-22 | 2019-12-31 | 航天海鹰安全技术工程有限公司 | Urban ponding prediction and safety early warning system based on drainage model |
| CN111651885A (en) * | 2020-06-03 | 2020-09-11 | 南昌工程学院 | Intelligent sponge urban flood forecasting method |
| CN112016831A (en) * | 2020-08-27 | 2020-12-01 | 西安易辑数字科技有限公司 | AI intelligent forecast-based urban waterlogging landing area identification method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10962682B2 (en) * | 2015-12-15 | 2021-03-30 | Wuhan University | System and method for forecasting floods |
-
2021
- 2021-01-15 CN CN202110057607.3A patent/CN112785053B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009300311A (en) * | 2008-06-16 | 2009-12-24 | Toshiba Corp | Flood prediction support device and method for supporting prediction of flood |
| KR20100131902A (en) * | 2009-06-08 | 2010-12-16 | 부경대학교 산학협력단 | Flood prediction information system of high-resolution rapid-updated-coupling and flood prediction information method of high- resolution rapid-updated-coupling |
| RU2548920C1 (en) * | 2013-10-18 | 2015-04-20 | Республика Саха (Якутия), от имени которой выступает уполномоченное Государственным комитетом Республики Саха (Якутия) по инновационной политике и науке лицо - Государственное бюджетное учреждение "Академия наук Республики Саха (Якутия)" (ГБУ АН РС(Я)) | Method of predicting hydrologic environment on obstruction-hazard parts of river |
| CN107220496A (en) * | 2017-05-26 | 2017-09-29 | 上海市气象灾害防御技术中心 | A kind of urban rainstorm waterlogging assesses modeling method |
| JP2019148058A (en) * | 2018-02-26 | 2019-09-05 | 三菱電機株式会社 | Flooding prediction evaluation device |
| CN108984823A (en) * | 2018-05-31 | 2018-12-11 | 北京市水科学技术研究院 | A kind of determination method of combined sewer overflow storage pond scale |
| CN110633865A (en) * | 2019-09-22 | 2019-12-31 | 航天海鹰安全技术工程有限公司 | Urban ponding prediction and safety early warning system based on drainage model |
| CN111651885A (en) * | 2020-06-03 | 2020-09-11 | 南昌工程学院 | Intelligent sponge urban flood forecasting method |
| CN112016831A (en) * | 2020-08-27 | 2020-12-01 | 西安易辑数字科技有限公司 | AI intelligent forecast-based urban waterlogging landing area identification method |
Non-Patent Citations (8)
| Title |
|---|
| A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction;Mohammed Khalaf，等;2018 IEEE Congress on Evolutionary Computation;1-8 * |
| 利用洪涝模型进行城市内涝风险快速识别与预警;徐美，等;武汉大学学报(信息科学版);第45卷(第08期);1185-1194 * |
| 基于多模型耦合的城市内涝多视角分析;李尤，等;中国农村水利水电(第05期);82-90 * |
| 基于精细化排水数值模型的流域海绵规划研究——以清河流域为例;邸苏闯，等;北京水务(第03期);24-29 * |
| 基于降水时空分布情景模拟的暴雨洪涝致灾危险性评价;姜智怀，等;暴雨灾害;第35卷(第05期);464-470 * |
| 成都市中心城区暴雨内涝模拟及内涝特征分析;曾鹏，等;中国水利水电科学研究院学报;第18卷(第03期);232-239 * |
| 暴雨内涝危险性情景模拟方法研究――以上海中心城区为例;黄清雨，等;地球信息科学学报;第18卷(第04期);506-513 * |
| 设计暴雨雨型对城市内涝影响数值模拟;侯精明，等;水科学进展;第28卷(第06期);820-828 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112785053A (en) | 2021-05-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112785053B (en) | Method and system for forecasting urban drainage basin flood | |
| Madsen et al. | Update of regional intensity–duration–frequency curves in Denmark: Tendency towards increased storm intensities | |
| Moreira et al. | SPI-based drought category prediction using loglinear models | |
| Fan et al. | Flood forecasting on the Tocantins River using ensemble rainfall forecasts and real‐time satellite rainfall estimates | |
| Svensson et al. | Review of rainfall frequency estimation methods | |
| Shi et al. | Physically based simulation of the streamflow decrease caused by sediment‐trapping dams in the middle Yellow River | |
| Kyselý et al. | Comparison of regional and at‐site approaches to modelling probabilities of heavy precipitation | |
| Green et al. | The revised intensity-frequency-duration (IFD) design rainfall estimates for Australia–An overview | |
| Cardoso et al. | Generation of intensity duration frequency curves and intensity temporal variability pattern of intense rainfall for Lages/SC | |
| CN115630337A (en) | Quantitative evaluation method and system for extreme rainfall attribution based on large-scale climate remote correlation | |
| Verstraten et al. | Sensitivity of Australian roof drainage structures to design rainfall variability and climatic change | |
| Tunas | The application of ITS-2 model for flood hydrograph simulation in large-size rainforest watershed, Indonesia | |
| Abdulla et al. | Assessment of the impact of potential climate change on the surface water of a trans-boundary basin: Case study Yarmouk River | |
| Kyselý et al. | Return periods of the August 2010 heavy precipitation in northern Bohemia (Czech Republic) in the present climate and under climate change | |
| Zhang et al. | Risk assessment on storm flood disasters of different return periods in Huai River Basin | |
| KR101843008B1 (en) | Decision method and system for investment of urban flood control facility using of climate chang scenarios | |
| Pekárová et al. | Flood regime of rivers in the Danube River basin | |
| Vo et al. | Climate Change Impact on Urban Flooding in Quynhon City, Vietnam | |
| Lin et al. | Risk assessment of dam-breach flood under extreme storm events | |
| Afrin et al. | Assessment of future flow of Hatirjheel-Begunbari drainage system due to climate change | |
| Kim et al. | Hazard analysis of typhoon-related external events using extreme value theory | |
| Parvaze et al. | Flood forecasting in the sparsely gauged jhelum river basin of greater himalayas using integrated hydrological and hydraulic modelling approach | |
| Gharib et al. | Calculation of the spatial flooding intensity with unit flood response method in the Tangrah Watershed, Iran | |
| Fan et al. | Operational flood forecasting system to the Uruguay River Basin using the hydrological model MGB-IPH | |
| Carneiro et al. | Hydrological risk of dam failure under climate change |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |