Flood risk early warning grading standard method based on urban flood simulation forecast

Abstract

The invention discloses a flood risk early warning grading standard method based on urban flood simulation forecast. The method is specifically implemented according to the following steps: step 1, constructing a grid city model; step 2, evaluating the disaster-bearing object capability of the grid city model in the step 1; 3, evaluating the waterlogging prevention capability in each grid unit in the grid city model in the step 1; 4, constructing a flood risk assessment model; step 5, constructing a flood risk grading early warning model, quantifying a risk value and a continuous trend of flood, and setting a triggering condition of risk early warning; and step 6, constructing a grading early warning model. According to the method, the problems that the dynamic process of urban flood occurrence cannot be accurately simulated and the matching degree with actual risk early warning is low in an existing grading method are solved, the urban flood risk grade can be accurately predicted and evaluated, and the accuracy and timeliness of early warning are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of flood risk early warning, and relates to a flood risk early warning grading standard method based on urban flood simulation and prediction. BACKGROUND

[0002] With the increasing frequency of urban flood disasters, urban flood disasters are often caused by excessive rainfall due to heavy rain or long-term rainfall, low terrain, and large amounts of accumulated water that cannot be timely drained. As one of the serious natural disasters faced by cities, urban floods pose a serious threat to people's life and property safety and urban development. In order to improve the management capability of urban flood disasters, the existing technology usually adopts an urban flood numerical model, that is, a numerical model simulating urban flood processes is established by using computer technology. By using theories of hydraulics and hydrology, rainfall / inflow, terrain, land use type, etc. are taken as input files to simulate and calculate the flood disaster caused by urban flooding due to heavy rain or flood inundation, and finally obtain information of hydraulic elements such as flow, water depth, flow velocity, and inundation duration and range in the flood disaster. Clear flood risk early warning grading standards provide a basis for the formulation of urban flood control and drainage schemes, disaster relief decision-making, etc., so that appropriate measures can be taken according to different risk levels to reduce the impact on urban areas.

[0003] The existing flood risk early warning grading standards mostly rely on static grading standards, which divide the warning levels by setting fixed thresholds based on historical disaster data. The static grading standards focus on a single hydrological indicator (such as river water level exceeding the warning value), without integrating multi-dimensional dynamic factors such as vulnerability of disaster-affected bodies (such as building flood resistance) and drainage capacity, resulting in a disconnect between the grading results and the actual disaster impact. The existing urban flood model cannot accurately simulate the dynamic process of urban flood occurrence, and cannot be used for real-time simulation and prediction of urban flood disasters.

[0004] With the application of GIS technology, static grading standards are introduced into grid analysis, but the grid division granularity is fixed and cannot be dynamically adjusted according to regional characteristics (such as low-lying areas and business districts), making it difficult to reflect local risk differences. Moreover, the risk assessment and early warning grading stages are disconnected, the risk value output by the assessment model is not directly related to the early warning trigger mechanism, the grading results have low matching degree with the actual risk, and the early warning threshold interval is static and not updated in real time with the monitoring data, resulting in insufficient timeliness and pertinence of the early warning response and the problem of dynamic adaptation of risk assessment values and early warning grading. SUMMARY

[0005] The purpose of the present application is to provide a flood risk early warning grading standard method based on urban flood simulation and prediction, which solves the problem of inaccurate simulation of the dynamic process of urban flood occurrence and low matching degree with actual risk warning in the existing grading method.

[0006] The technical scheme adopted by the present application is a flood risk early warning grading standard method based on urban flood simulation and prediction, which is specifically implemented according to the following steps: Step 1, constructing a grid-based city model; Step 2, evaluating the disaster-bearing object capacity of the grid-based city model in step 1; Step 3, evaluating the flood control capacity in each grid unit in the grid-based city model in step 1; Step 4, constructing a flood risk assessment model; Step 5, constructing a flood risk grading early warning model, quantifying the risk value and continuous trend of the flood, and setting the triggering condition of the risk early warning; Step 6, constructing a grading early warning model.

[0007] The present application is also characterized in that, Step 1 specifically includes, Step 1.1, obtaining the basic data of the current target area; Step 1.2, using geographic information system (GIS) technology to obtain geographic data of remote sensing images of the current target area, including building height, green cover, and population density.

[0008] Step 1.3, dividing the target area into several grid units; Step 1.4, assigning the geographic data in step 1.2 to the corresponding grid units in step 1.3 to construct a grid-based city model.

[0009] Step 2 specifically includes, Step 2.1, dividing the land types of the grid units into four types: residential area, commercial area, industrial area, and agricultural area; Step 2.2, setting the carrying capacity evaluation index of different land types in step 2.1 according to the characteristics and needs of the population density, traffic flow, water resource utilization, and environmental quality of the evaluation object; Step 2.3, statistically analyzing the damage of each grid unit under different intensity of flood disasters, and determining the damage probability of each grid unit under different disaster intensity by frequency analysis combined with the carrying capacity evaluation index in step 2.2; Step 2.4, determining the flood disaster damage rate of historical disaster-bearing objects combined with the damage probability in step 2.4; Step 2.5, performing disaster damage rate-disaster intensity relationship analysis through correlation analysis and regression analysis, and predicting the disaster damage rate of disaster-bearing objects under the known disaster intensity.

[0010] Step 3 specifically includes, Step 3.1, evaluate the carrying capacity of each grid unit drainage system and the construction of emergency plan, analyze the implementation and effect of emergency management measures in flood disaster; Step 3.2, combined with the flood damage rate of disaster bearing object in step 2, the evaluation results of drainage system in step 3.1 and the emergency management capacity evaluation results, the flood control capacity of each grid unit is comprehensively evaluated, and the flood control capacity score is obtained.

[0011] Step 4 is, Step 4.1, obtain the determined grade standard in the grid city model, set the grade standard of flood risk according to the disaster bearing capacity of disaster bearing object evaluated in step 2 and the evaluation results of flood control capacity of grid unit in step 3; Step 4.2, according to the grade standard of flood risk set in step 4.1, using GIS spatial analysis model, flood risk assessment model is constructed, and the flood risk grade of each grid unit under different rainfall conditions is simulated.

[0012] Step 4.2 is, Step 4.2.1, in the GIS spatial analysis model, input the basic data of each grid unit obtained in step 1 and the flood control capacity score in step 3.2, construct the flood risk assessment model, the flood risk assessment model couples "rainfall-runoff-flood-loss", outputs the risk grade of each grid unit, and shows the flood risk of each grid unit under different rainfall conditions; Step 4.2.2, use historical flood disaster data to verify the flood risk assessment model, verify the accuracy and reliability of the results of the flood risk assessment model, and then optimize and adjust the model to improve the prediction ability of the flood risk assessment model.

[0013] Step 5 is, Step 5.1, according to the flood risk assessment model in step 4, the flood risk of each grid unit is quantified, and multiple factors affecting the flood risk are integrated into a comprehensive risk assessment index, and each grid unit corresponds to the calculation of corresponding flood risk value; Step 5.2, according to the time change of the flood risk value in step 5.1, judge the trend of flood risk, and set different risk warning trigger conditions corresponding to different levels.

[0014] Step 5.2 divides the risk value into intervals, different risk levels correspond to different threshold intervals, and the risk level is divided into low risk, medium risk, high risk and extremely high risk. When the flood risk value of a certain grid unit reaches or exceeds the threshold value of a certain level, the corresponding level of warning is triggered.

[0015] Step 6 is, Step 6.1, the hierarchical early warning model comprises four parts of data input, risk assessment, early warning triggering and result output, and the output of the risk assessment model is converted into a quantitative index and a triggering mechanism directly used for early warning, so that the connection from risk assessment to early warning response is realized; Step 6.2, according to the early warning information obtained in step 6.1, a corresponding hierarchical early warning response mechanism is formulated, and corresponding response measures are started.

[0016] Step 6.1, the data input part collects and processes real-time monitoring or prediction data; the risk assessment part calculates the flood risk value of each grid unit according to the input data; the early warning triggering part judges whether early warning needs to be triggered according to the set triggering condition; and the result output part is responsible for generating a risk distribution map and other early warning information.

[0017] The beneficial effects of the present application are: the flood risk early warning hierarchical standard method based on urban flood simulation forecasting can accurately predict and evaluate the urban flood risk grade, so as to timely issue early warning, provide valuable response time for city managers and residents, effectively reduce the loss caused by flood disasters, and improve the accuracy and timeliness of early warning; scientific and reasonable early warning hierarchical standard and response scheduling strategy are formulated, which helps city managers to take corresponding disaster prevention and mitigation measures according to the risk grade, can quickly start corresponding response measures, and improves the urban flood control and disaster mitigation capacity; effectively reduces the threat of urban flood disasters to infrastructure and the safety of life and property of residents, and safeguards the safety and stability of the city; the results of urban waterlogging disaster risk assessment can be widely applied to disaster management, disaster early warning, emergency rescue and other aspects, and have important significance for urban disaster prevention and mitigation. DETAILED DESCRIPTION

[0018] The present application will be described in detail below in combination with specific embodiments.

[0019] Embodiment 1 The flood risk early warning hierarchical standard method based on urban flood simulation forecasting is implemented according to the following steps: Step 1, a grid-based city model is constructed; Step 2, the disaster-bearing object capacity of the grid-based city model in step 1 is evaluated; Step 3, the waterlogging prevention capacity in each grid unit in the grid-based city model in step 1 is evaluated; Step 4, a flood risk assessment model is constructed; Step 5, a flood risk hierarchical early warning model is constructed, the risk value and the continuous trend of the flood are quantified, and the triggering condition of the risk early warning is set; Step 6, a hierarchical early warning model is constructed.

[0020] Embodiment 2 The application is based on a flood risk early warning grading standard method for urban flood simulation and prediction, and is implemented according to the following steps: Step 1, constructing a gridded urban model; Step 1.1, obtaining basic data of the current target area; The basic data includes rainfall measurement data, rainfall forecast data, river flow measurement data, river flow forecast data, and historical record data related to flood disasters in the target area.

[0021] The historical record data includes information records of casualties and economic losses caused by the integration of basic data; the historical record data is used to statistically analyze the damage of each region under different intensity flood disasters, and provides a basis for calculating the damage probability and damage rate.

[0022] Step 1.2, obtaining geographic data of remote sensing images of the current target area by using geographic information system (GIS) technology, the geographic data including building height, green coverage, and population density.

[0023] Step 1.3, dividing the target area into a plurality of grid cells; According to the characteristics and requirements of the basic data in the target area in step 1.1, the size of the grid cell is determined, and the grid division tool in the GIS software is used for division; Step 1.4, assigning the geographic data in step 1.2 to the corresponding grid cells in step 1.3 to construct a gridded urban model.

[0024] Step 2, evaluating the disaster-bearing object capacity of the gridded urban model in step 1; Step 3, evaluating the flood control capacity of each grid cell in the gridded urban model in step 1; Step 4, constructing a flood risk assessment model; Step 5, constructing a flood risk grading early warning model, quantifying the risk value and continuous trend of flood, and setting the trigger condition of risk early warning; Step 6, constructing a grading early warning model.

[0025] Example 3 The application is based on a flood risk early warning grading standard method for urban flood simulation and prediction, and is implemented according to the following steps: Step 1, constructing a gridded urban model; Step 1.1, obtaining basic data of the current target area; The basic data includes rainfall measurement data, rainfall forecast data, river flow measurement data, river flow forecast data, and historical record data related to flood disasters in the target area.

[0026] The historical record data includes the information record of the casualties and economic losses caused as basic data integration; and the historical record data is used for counting the damage conditions of each region under different intensity flood disasters, and providing a basis for calculating the damage probability and damage rate.

[0027] Step 1.2, geographical data of the current target region remote sensing image is obtained by using the geographic information system (GIS) technology, and the geographical data includes building height, green land coverage and population density.

[0028] Step 1.3, the target region is divided into a plurality of grid units; According to the characteristics and requirements of the basic data in the target region in step 1.1, the size of the grid unit is determined, and the grid division tool in the GIS software is used for division; Step 1.4, the geographical data in step 1.2 is assigned to the corresponding grid unit in step 1.3, and the grid city model is constructed.

[0029] Step 2, the carrying capacity of the grid city model in step 1 is evaluated; Step 2.1, the land type of the grid unit is divided into four types of residential area, commercial area, industrial area and agricultural area; Step 2.2, according to the characteristics and requirements of the population density, traffic flow, water resource utilization and environmental quality of the evaluation object, the carrying capacity evaluation index of different land types in step 2.1 is set; Step 2.3, the damage conditions of each grid unit under different intensity flood disasters are counted, the damage probability of each grid unit under different disaster intensity is determined through frequency analysis combined with the carrying capacity evaluation index in step 2.2; Step 2.4, the flood damage rate of the historical carrying object is determined combined with the damage probability in step 2.4; Step 2.5, the relationship between the damage rate and the disaster intensity is analyzed through correlation analysis and regression analysis, and the damage rate of the carrying object is predicted under the known disaster intensity.

[0030] Step 3, the flood control capacity of each grid unit in the grid city model in step 1 is evaluated; Step 4, a flood risk assessment model is constructed; Step 5, a flood risk grading early warning model is constructed, the risk value and the continuous trend of the flood are quantified, and the triggering condition of the risk early warning is set; Step 6, a grading early warning model is constructed.

[0031] Example 4 The flood risk early warning grading standard method based on the city flood simulation prediction is implemented according to the following steps. Step 1, constructing a grid city model; Step 1.1, obtaining basic data of the current target area; The basic data includes rainfall observation data, rainfall forecast data, river flow observation data, river flow forecast data, and historical record data related to flood disasters in the target area.

[0032] The historical record data includes information records of casualties and economic losses caused by floods, which are integrated as basic data. The historical record data is used to statistically analyze the damage of each region under different intensity of flood disasters, providing a basis for calculating the damage probability and disaster damage rate.

[0033] Step 1.2, using geographic information system (GIS) technology to obtain geographic data of remote sensing images of the current target area, including building height, green cover, and population density.

[0034] Step 1.3, dividing the target area into several grid cells; According to the characteristics and requirements of the basic data in the target area in step 1.1, determine the size of the grid cell, and use the grid division tool in the GIS software to divide it; Step 1.4, assign the geographic data in step 1.2 to the corresponding grid cells in step 1.3 to construct a grid city model.

[0035] Step 2, evaluating the capacity of disaster-bearing objects in the grid city model in step 1; Step 2.1, dividing the land types of the grid cells into residential areas, commercial areas, industrial areas, and agricultural areas; Step 2.2, setting the carrying capacity evaluation index for different land types in step 2.1 according to the characteristics and requirements of population density, traffic flow, water resource utilization, and environmental quality of the evaluation object; Step 2.3, statistically analyzing the damage of each grid cell under different intensity of flood disasters, and determining the damage probability of each grid cell under different disaster intensity through frequency analysis combined with the carrying capacity evaluation index in step 2.2; Step 2.4, determining the flood disaster damage rate of historical disaster-bearing objects combined with the damage probability in step 2.4; Step 2.5, conducting a disaster damage rate-disaster intensity relationship analysis through correlation analysis and regression analysis, and predicting the disaster damage rate of disaster-bearing objects under known disaster intensity.

[0036] Step 3, evaluating the flood control capacity of each grid cell in the grid city model in step 1; Step 3.1, evaluate the bearing capacity of each grid unit drainage system and the construction of emergency plan, analyze the implementation and effect of emergency management measures in flood disaster; The bearing capacity includes the operation of infrastructure in flood disaster, drainage efficiency, and flood control effect; The construction of emergency plan includes early warning system, evacuation route, and rescue team.

[0037] Step 3.2, based on the flood damage rate of the disaster-bearing object in step 2, the evaluation results of the drainage system in step 3.1 and the emergency management capacity evaluation results, the flood control capacity of each grid unit is comprehensively evaluated, and the flood control capacity score is obtained.

[0038] Step 4, build a flood risk assessment model; Step 5, build a flood risk grading early warning model, quantify the risk value and continuous trend of flood, and set the trigger condition of risk warning; Step 6, build a grading early warning model.

[0039] Example 5 The present application is based on the flood risk early warning grading standard method of urban flood simulation and prediction, which is implemented according to the following steps: Step 1, build a grid city model; Step 1.1, obtain the basic data of the current target area; The basic data includes rainfall measurement data, rainfall forecast data, river flow measurement data, river flow forecast data, and historical record data related to the target area flood disaster.

[0040] The historical record data includes the information record of the casualties and economic losses caused by the basic data integration; the historical record data is used to statistically analyze the damage of each region under different intensity flood disasters, and provides basis for calculating the damage probability and damage rate.

[0041] Step 1.2, use geographic information system (GIS) technology to obtain geographic data of remote sensing image of the current target area, the geographic data includes building height, green coverage, population density.

[0042] Step 1.3, divide the target area into several grid units; According to the characteristics and requirements of the basic data in the target area in step 1.1, determine the size of the grid unit, use the grid division tool in the GIS software to divide; Step 1.4, assign the geographic data in step 1.2 to the corresponding grid unit in step 1.3 to build a grid city model.

[0043] Step 2, evaluate the disaster-bearing object capacity of the grid city model in step 1; Step 2.1, divide the land types of the grid cells into residential, commercial, industrial, and agricultural areas; Step 2.2, according to the characteristics and needs of the population density, traffic flow, water resource utilization, and environmental quality of the evaluation object, set the carrying capacity evaluation indexes of different land types in step 2.1; Step 2.3, count the damage of each grid cell under different intensity of flood disasters, through frequency analysis, combined with the carrying capacity evaluation indexes in step 2.2, determine the damage probability of each grid cell under different disaster intensity; Step 2.4, combined with the damage probability in step 2.4, determine the flood damage rate of historical disaster-bearing objects; Step 2.5, through correlation analysis and regression analysis, analyze the relationship between disaster damage rate and disaster intensity, and predict the disaster damage rate of disaster-bearing objects under known disaster intensity.

[0044] Step 3, evaluate the flood control capacity of each grid cell in the grid city model in step 1; Step 3.1, evaluate the carrying capacity and emergency plan construction of the drainage system of each grid cell, analyze the implementation and effect of emergency management measures in flood disasters; The carrying capacity includes the operation status, drainage efficiency, and flood control effect of infrastructure in flood disasters; Emergency plan construction includes early warning system, evacuation route, and rescue team.

[0045] Step 3.2, combined with the flood damage rate of disaster-bearing objects in step 2, the evaluation results of drainage system in step 3.1, and the emergency management capacity evaluation results, comprehensively evaluate the flood control capacity of each grid cell, and get the flood control capacity score.

[0046] Step 4, build a flood risk assessment model; Step 4.1, obtain the determined grade standards in the grid city model, according to the carrying capacity of disaster-bearing objects evaluated in step 2 and the evaluation results of flood control capacity of grid cells in step 3, set the grade standards of flood risk; Divide the risk level into four levels: low risk, medium risk, high risk, and extremely high risk, set the water depth and duration threshold of each level, each level corresponds to different disaster intensity and loss degree.

[0047] Step 4.2, according to the grade standards of flood risk set in step 4.1, use GIS spatial analysis model to build flood risk assessment model, simulate the flood risk level of each grid cell under different rainfall conditions.

[0048] Step 4.2.1, in the GIS spatial analysis model, input the basic data of each grid unit obtained in step 1, the waterlogging prevention capacity score in step 3.2, build a flood risk assessment model, and the flood risk assessment model couples “rainfall-runoff-flooding-loss”, outputs the risk level of each grid unit, and shows the flood risk of each grid unit under different rainfall conditions; Step 4.2.2, verify the flood risk assessment model using historical flood disaster data, verify the accuracy and reliability of the results of the flood risk assessment model, and then optimize and adjust the model to improve the prediction ability of the flood risk assessment model.

[0049] Step 5, build a flood risk grading early warning model to quantify the risk value and continuous trend of flood, and set the trigger condition of risk early warning; Step 5.1, according to the flood risk assessment model in step 4, the flood risk of each grid unit is quantified, and multiple factors affecting the flood risk are integrated into a comprehensive risk assessment index, and each grid unit corresponds to a calculated flood risk value; The multiple factors affecting the flood risk include rainfall, terrain, soil type, water system distribution and building density.

[0050] Step 5.2, according to the time change of the flood risk value in step 5.1, the trend of the flood risk is judged, and the trigger condition of different levels of risk early warning is set.

[0051] The risk value is divided into intervals, different risk levels correspond to different threshold intervals, and the risk levels are divided into low risk, medium risk, high risk and extremely high risk. When the flood risk value of a certain grid unit reaches or exceeds the threshold value of a certain level, the early warning of the corresponding level is triggered.

[0052] Step 6, build a grading early warning model.

[0053] Embodiment 6 The application provides a flood risk early warning grading standard method based on urban flood simulation prediction, which is implemented according to the following steps: Step 1, build a grid urban model; Step 1.1, obtain the basic data of the current target area; The basic data includes rainfall measurement data, rainfall forecast data, river flow measurement data, river flow forecast data, and historical record data related to flood disasters in the target area.

[0054] The historical record data includes the information record of the casualties and economic losses caused by the basic data integration; the historical record data is used to statistically analyze the damage of each region under different intensity flood disasters, and provides a basis for calculating the damage probability and damage rate.

[0055] Step 1.2, using geographic information system (GIS) technology, obtain the geographic data of the current target area remote sensing image, the geographic data includes building height, green coverage, population density.

[0056] Step 1.3, divide the target area into several grid units; According to the characteristics and requirements of the basic data in the target area in step 1.1, determine the size of the grid unit, use the grid division tool in the GIS software to divide; Step 1.4, assign the geographic data in step 1.2 to the corresponding grid unit in step 1.3, and construct the grid city model.

[0057] Step 2, evaluate the disaster-bearing object capacity of the grid city model in step 1; Step 2.1, divide the land type of the grid unit into four types: residential area, commercial area, industrial area and agricultural area; Step 2.2, according to the characteristics and requirements of the population density, traffic flow, water resource utilization and environmental quality of the evaluation object, set the carrying capacity evaluation index of different land types in step 2.1; Step 2.3, statistics the damage situation of each grid unit under different intensity of flood disaster, through frequency analysis, combined with the carrying capacity evaluation index in step 2.2, determine the damage probability of each grid unit under different disaster intensity; Step 2.4, combined with the damage probability in step 2.4, determine the flood disaster damage rate of historical disaster-bearing object; Step 2.5, through correlation analysis and regression analysis, analyze the relationship between disaster damage rate and disaster intensity, and predict the disaster damage rate of disaster-bearing object under the known disaster intensity.

[0058] Step 3, evaluate the flood control capacity of each grid unit in the grid city model in step 1; Step 3.1, evaluate the carrying capacity and emergency plan construction of the drainage system of each grid unit, analyze the implementation and effect of emergency management measures in flood disaster; The carrying capacity includes the operation status of infrastructure in flood disaster, drainage efficiency and flood control effect; Emergency plan construction includes early warning system, evacuation route and rescue team.

[0059] Step 3.2, combined with the flood disaster damage rate of disaster-bearing object in step 2, the evaluation results of drainage system in step 3.1 and the emergency management capacity evaluation results, comprehensively evaluate the flood control capacity of each grid unit, and get the flood control capacity score.

[0060] Step 4, build a flood risk assessment model; Step 4.1, obtain the established grading standards in the gridded urban model, and set the grading standards for flood risk according to the assessment results of the disaster-bearing capacity of disaster-bearing objects in step 2 and the assessment results of the flood control capacity of grid cells in step 3; The risk level is divided into four levels: low risk, medium risk, high risk and extremely high risk. The corresponding water depth and duration threshold of each level is set. Each level corresponds to different disaster intensity and loss degree.

[0061] Step 4.2, according to the grading standards of flood risk set in step 4.1, use GIS spatial analysis model to build flood risk assessment model, simulate the flood risk level of each grid cell under different rainfall conditions.

[0062] Step 4.2.1, in the GIS spatial analysis model, input the basic data of each grid cell obtained in step 1 and the flood control capacity score in step 3.2, build the flood risk assessment model. The flood risk assessment model couples "rainfall-runoff-flood-loss", outputs the risk level of each grid cell, and shows the flood risk of each grid cell under different rainfall conditions; Step 4.2.2, use historical flood disaster data to verify the flood risk assessment model, verify the accuracy and reliability of the results of the flood risk assessment model, and then optimize and adjust the model to improve the prediction ability of the flood risk assessment model.

[0063] Step 5, build a flood risk grading early warning model to quantify the risk value and the trend of flood, and set the trigger condition of risk early warning; Step 5.1, according to the flood risk assessment model in step 4, quantify the flood risk of each grid cell, integrate multiple factors affecting flood risk into a comprehensive risk assessment index, and calculate the corresponding flood risk value for each grid cell; The multiple factors affecting flood risk include rainfall, terrain, soil type, water system distribution and building density.

[0064] Step 5.2, according to the time change of the flood risk value in step 5.1, judge the trend of flood risk, and set the trigger condition of risk early warning corresponding to different levels.

[0065] The risk value is divided into intervals, different risk levels correspond to different threshold intervals, and the risk level is divided into low risk, medium risk, high risk and extremely high risk. When the flood risk value of a certain grid cell reaches or exceeds the threshold value of a certain level, the early warning of the corresponding level is triggered.

[0066] Step 6, build a grading early warning model; Step 6.1, the hierarchical early warning model includes four parts of data input, risk assessment, early warning trigger and result output. By converting the output of the risk assessment model into quantitative indicators and trigger mechanisms directly used for early warning, the connection from risk assessment to early warning response is realized. The data input part collects and processes real-time monitoring or prediction data; the risk assessment part calculates the flood risk value of each grid cell according to the input data; the early warning trigger part judges whether early warning is needed according to the set trigger conditions; the result output part is responsible for generating risk distribution map and other early warning information.

[0067] Step 6.2, according to the early warning information obtained in step 6.1, the corresponding hierarchical early warning response mechanism is formulated, and the corresponding response measures are started; When the early warning level is low risk, monitoring and early warning are strengthened; when the early warning level is medium risk, drainage facilities are started, residents in low-lying areas are evacuated, etc.; when the early warning level is high risk or extremely high risk, the emergency response mechanism is immediately started, and measures such as shutdown, stop production, stop operation and transfer to avoid risk are taken.

[0068] Through the network online real-time or regular acquisition of the required dynamic data, real-time or regular display or push to users through the visual network platform.

[0069] Dynamic data includes real-time rainfall, river water level, drainage system operation parameters, which are introduced into the flood risk assessment model and the hierarchical early warning model respectively, to update the risk assessment value of each grid cell in real time and ensure that it reflects the current latest flood risk state; according to the characteristics of real-time data, the threshold interval of each level is dynamically optimized; the four-level classification system of low risk, medium risk, high risk and extremely high risk remains unchanged, ensuring that the corresponding response mechanism (such as strengthening monitoring for low risk and starting emergency evacuation for high risk) can be stably executed. Using geographic information system (GIS) as the platform, the flood risk assessment model and the early warning trigger condition are integrated into GIS, and the spatial analysis function of GIS intuitively shows the flood risk area. When the real-time monitoring or prediction data reaches the preset trigger condition, the model automatically triggers the early warning of the corresponding level, and generates the risk distribution map and other early warning information, so as to take measures to respond.

[0070] The application provides a flood risk early warning grading standard method based on urban flood simulation prediction, comprising the following steps: in step 1, real-time monitoring data, historical disaster data and geographic spatial data are integrated to provide standardized and high-precision data input for subsequent grid model construction, risk assessment and early warning model building, so as to ensure the accuracy and pertinence of subsequent analysis. The spatial data integration capability and dynamic grid division technology of GIS are adopted to fuse real-time, historical and spatial multi-dimensional data, so as to avoid the limitation of single data source and improve the comprehensiveness of data coverage; dynamic grid division makes the high-risk area assessment more fine, lays a foundation for subsequent risk grading of grid unit level and enhances the controllability of spatial precision.

[0071] In step 2, the vulnerability of each grid unit, the bearing capacity of the drainage system and the emergency management level are analyzed in multiple dimensions, the flood control capacity is quantified, and key parameters are provided for subsequent risk level division and model construction. The historical data statistical analysis method is adopted to realize the prediction of the damage degree of the disaster-bearing body, the comprehensive evaluation method is adopted to obtain the flood control capacity score, the evaluation dimension is comprehensive, both natural factors and human factors are considered, and the flood control capacity score is more practical; the relationship model constructed based on historical data can quantitatively predict the damage, provide calculable basic parameters for risk assessment and improve the scientificity of evaluation.

[0072] In step 4, the flood control capacity evaluation result is combined with the risk level standard to construct a model that can simulate the risk level of the grid unit under different rainfall conditions, and the quantitative evaluation and prediction of the flood risk are realized. The GIS spatial analysis model is adopted, the grid unit is taken as the basis, the basic data and the flood control capacity score are integrated, the “rainfall-runoff-flooding-loss” coupling model is constructed, the model verification and optimization technology is adopted, the historical flood data is used to verify the accuracy of the model, and the model is optimized through parameter adjustment, and the advantages are as follows: the model can simulate different rainfall scenarios, predict the risk distribution in advance and provide forward-looking basis for early warning; the risk level is visualized in space by combining the GIS technology, so that the evaluation result is more intuitive and the high-risk area is easy to identify.

[0073] The risk value quantification technology is adopted to integrate multiple influence factors to calculate the risk value of the grid unit through a comprehensive index, and the risk change trend is analyzed; the early warning trigger mechanism design technology is adopted: based on the risk value, threshold intervals are divided, and trigger conditions are set in combination with the change trend; the advantages are as follows: the risk value quantification makes the early warning standard calculable and comparable, avoids subjective judgment errors, the trend analysis is included in the trigger condition, and the rising trend of the early warning risk is predicted in advance to gain time for emergency response.

[0074] The application provides a flood risk early warning grading standard method based on urban flood simulation prediction, breaks through the limitation of traditional administrative region scale evaluation by constructing a gridded urban model, divides the city into small grid units and assigns multi-dimensional data, realizes fine risk analysis at the grid unit level, combines multi-factor comprehensive evaluation such as disaster body damage rate and drainage system capacity, makes the risk evaluation no longer rely on a single hydrological index, but more closely matches the actual disaster impact, greatly improves the matching degree of the evaluation result and the real risk, relies on a real-time data access mechanism, substitutes dynamic data such as real-time rainfall and river water level into the risk evaluation model and the grading early warning model, constantly updates the risk value and dynamically optimizes the threshold interval, solves the problem that the traditional static threshold cannot adapt to sudden changes. At the same time, by analyzing the trend of the risk value, the early warning can be triggered in advance, time is gained for emergency response, and early warning lag is avoided; a complete closed loop of 'risk assessment-grading early warning-response mechanism' is constructed, the four-level risk grade corresponds to clear response measures, so that the early warning information can be directly converted into executable disaster prevention and reduction actions. This linkage mechanism avoids the problem of disconnection between early warning and response, ensures that each department can quickly cooperate according to the early warning level, improves the efficiency of emergency disposal; the model is verified and optimized through historical flood data, ensuring the prediction accuracy of the model under different rainfall conditions; at the same time, dynamic grid division can adjust the precision according to the characteristics of the city, taking into account the fine analysis of high-risk areas and the overall calculation efficiency, so that the method is applicable to cities of different sizes and different terrains, and has wide practicality.

Claims

1. A method for classifying flood risk early warning based on urban flood simulation and forecasting, characterized in that, The specific steps are as follows: Step 1: Construct a gridded city model; Step 2: Evaluate the disaster-bearing capacity of the gridded city model from Step 1; Step 3: Evaluate the flood control capacity of each grid cell in the gridded city model from Step 1; Step 4: Construct a flood risk assessment model; Step 5: Construct a flood risk classification and early warning model, quantify the risk value and trend of floods, and set the trigger conditions for risk warnings; Step 6: Construct a tiered early warning model.

2. The flood risk early warning classification standard method based on urban flood simulation and forecasting as described in claim 1, characterized in that, Step 1 specifically involves: Step 1.1: Obtain basic data for the current target area; Step 1.2: Use Geographic Information System (GIS) technology to acquire geographic data of remote sensing images of the current target area; Step 1.3: Divide the target area into several grid cells; Step 1.4: Assign the geographic data from Step 1.2 to the corresponding grid cells in Step 1.3 to construct a gridded city model.

3. The flood risk early warning classification standard method based on urban flood simulation and forecasting as described in claim 2, characterized in that, Step 2 specifically involves: Step 2.1: Divide the land types of the grid units into four categories: residential area, commercial area, industrial area, and agricultural area; Step 2.2: Based on the characteristics and needs of the population density, traffic flow, water resource utilization, and environmental quality of the assessment objects, set the carrying capacity assessment indicators for different land types in Step 2.1; Step 2.3: Statistically analyze the damage of each grid unit under different intensities of flood disasters. Through frequency analysis and combined with the carrying capacity assessment index in Step 2.2, determine the probability of damage to each grid unit under different disaster intensities. Step 2.4: Based on the damage probability in Step 2.4, determine the flood damage rate of historically affected objects. Step 2.5: Through correlation analysis and regression analysis, analyze the relationship between disaster damage rate and disaster intensity, and predict the disaster damage rate of disaster-bearing objects given the disaster intensity.

4. The flood risk early warning classification standard method based on urban flood simulation and forecasting according to claim 3, characterized in that, Step 3 specifically involves: Step 3.1: Assess the capacity of the drainage system in each grid unit and the status of emergency response plans, and analyze the implementation and effectiveness of emergency management measures during floods; Step 3.2: Combining the flood damage rate of disaster-bearing objects in Step 2, the drainage system assessment results in Step 3.1, and the emergency management capability assessment results, a comprehensive assessment of the flood prevention capability of each grid unit is conducted to obtain a flood prevention capability score.

5. The flood risk early warning classification standard method based on urban flood simulation and forecasting as described in claim 4, characterized in that, Step 4 specifically involves: Step 4.1: Obtain the established grading standards in the gridded city model. Based on the assessment results of the disaster-bearing capacity of the disaster-bearing objects in Step 2 and the flood prevention capacity of the grid units in Step 3, set the grading standards for flood risk. Step 4.2: Based on the flood risk level standards set in Step 4.1, a flood risk assessment model is constructed using a GIS spatial analysis model to simulate the flood risk level of each grid unit under different rainfall conditions.

6. The flood risk early warning classification standard method based on urban flood simulation and forecasting as described in claim 5, characterized in that, Step 4.2 specifically involves: Step 4.2.1: In the GIS spatial analysis model, input the basic data of each grid unit obtained in Step 1 and the flood control capacity score in Step 3.2 to construct a flood risk assessment model. The flood risk assessment model couples "rainfall-runoff-inundation-loss" and outputs the risk level of each grid unit, showing the flood risk of each grid unit under different rainfall conditions. Step 4.2.2: Use historical flood disaster data to validate the flood risk assessment model, verify the accuracy and reliability of the flood risk assessment model results, and then optimize and adjust the model to improve the predictive ability of the flood risk assessment model.

7. The flood risk early warning classification standard method based on urban flood simulation and forecasting as described in claim 6, characterized in that, Step 5 specifically involves: Step 5.1: Based on the flood risk assessment model in Step 4, quantify the flood risk of each grid unit, integrate multiple factors affecting flood risk into a comprehensive risk assessment index, and calculate the corresponding flood risk value for each grid unit. Step 5.2: Based on the changes in flood risk value over time as described in Step 5.1, determine the trend of flood risk and set corresponding risk warning trigger conditions for different levels.

8. The flood risk early warning classification standard method based on urban flood simulation and forecasting as described in claim 7, characterized in that, Step 5.2 Divide the risk value into intervals. Different risk levels correspond to different threshold intervals. The risk levels are divided into low risk, medium risk, high risk and extremely high risk. When the flood risk value of a certain grid cell reaches or exceeds the threshold of a certain level, the corresponding level of warning is triggered.

9. The flood risk early warning classification standard method based on urban flood simulation and forecasting as described in claim 8, characterized in that, Step 6 specifically involves: Step 6.1: The hierarchical early warning model consists of four parts: data input, risk assessment, early warning triggering, and result output. By converting the output of the risk assessment model into quantitative indicators and triggering mechanisms that can be directly used for early warning, the connection between risk assessment and early warning response is realized. Step 6.2: Based on the early warning information obtained in Step 6.1, formulate a corresponding graded early warning response mechanism and initiate corresponding response measures.

10. The flood risk early warning classification standard method based on urban flood simulation and forecasting according to claim 9, characterized in that, Step 6.1 The data input section collects and processes real-time monitoring or forecast data; the risk assessment section calculates the flood risk value for each grid cell based on the input data. The warning triggering section determines whether a warning needs to be triggered based on the set triggering conditions; The output section is responsible for generating risk distribution maps and other early warning information.