CN117807917B - Loss function construction method and system based on scene flood disasters - Google Patents
Loss function construction method and system based on scene flood disasters Download PDFInfo
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Abstract
The invention discloses a loss function construction method and system based on scene flood disasters, comprising the steps of acquiring basic data of a research area and preprocessing; constructing a simulation model and calibrating model parameters; screening flood disaster loss evaluation indexes and constructing an evaluation index system; spatial spreading is carried out on the social and economic data; carrying out flood simulation to obtain rasterized ponding depth data of each time step; constructing and adopting a loss rate calculation module, calculating flood disaster losses at all moments based on the spread socioeconomic data, fitting a change curve of a flood disaster loss-time function, comparing fitting effects of different functions, and selecting the optimal fitting function and parameters thereof; and analyzing the relation between the related parameters and the disaster occurrence probability in the loss function, obtaining a relation formula of the loss function parameters and the disaster occurrence probability, and outputting the loss function. The dynamic change process of flood disaster loss can be reflected, and the indirect economic loss is calculated.
Description
Technical Field
The invention relates to a flood early warning technology, in particular to a loss function construction method and system based on a scene flood disaster.
Background
Urban flood disasters are caused by the phenomenon that water accumulation or overflow occurs in the urban in the rainfall process due to insufficient bearing capacity of an urban drainage system, and the urban social and economic activities and the life and property safety of people are seriously influenced. With the acceleration of global climate change and urban process, the frequency and severity of urban flood disasters are increased, and the caused economic loss also has the characteristics of dynamics, linkage and great disaster. Therefore, the method accurately evaluates the economic loss of the urban flood disasters, and has important theoretical and practical significance for formulating reasonable flood control measures, improving the disaster prevention and reduction capability of the cities and reducing the risks of the urban flood disasters.
At present, methods for evaluating urban flood disaster loss are mainly divided into two types: one is an evaluation method based on a hydrokinetic model, and the other is an evaluation method based on a loss function. According to the evaluation method based on the hydrologic hydrodynamic model, the loss of different types of assets and industries caused by flood disasters is calculated by simulating the space-time evolution process of the flood water conditions and combining with the socioeconomic data. The method can reflect the time-space dynamic change characteristics of flood disaster loss, but also has the defects of complex model, time consumption in calculation, large data demand and the like, and is unfavorable for real-time management and emergency response. And (3) based on the evaluation method of the loss function, the overall loss of the flood disaster is rapidly estimated by establishing the relation between the loss of the flood disaster and the reproduction period. The method can improve the timeliness of evaluation, but only gives a total loss value for storm flood in different reproduction periods, ignores the development process of flood disaster loss, lacks dynamic characteristics, and is difficult to support the dynamic management of disaster risks. In short, the loss function for the scene flood disasters is lacking, and the dynamic change process of the flood disaster loss cannot be reflected. Meanwhile, most of the research subjects are direct economic losses, and no loss function against indirect economic losses against flood disasters has been found yet.
Thus, further research and innovation is needed.
Disclosure of Invention
The invention aims to provide a loss function construction method based on a scene flood disaster, which aims to solve the problems in the prior art.
The technical scheme provides a loss function construction method based on scene flood disasters, which comprises the following steps:
s1, acquiring basic data of a research area and preprocessing the basic data;
Step S2, respectively constructing a one-dimensional hydrological hydrodynamic model of the river basin and a one-dimensional two-dimensional hydrological hydrodynamic coupling model of the city aiming at the river basin and the city, and calibrating model parameters;
S3, screening flood disaster damage assessment indexes, constructing a direct economic damage assessment index system and an indirect economic damage assessment index system, and performing space spreading on social economic data;
S4, carrying out flood simulation based on the urban one-dimensional two-dimensional hydrologic hydrodynamic coupling model after the calibration parameters to obtain rasterized ponding depth data of each time step; constructing and adopting a loss rate calculation module to obtain rasterized loss rate data of each time step; constructing loss evaluation modules of two time scales aiming at direct economic loss and indirect economic loss; based on the spread socioeconomic data, loss rate and industry relevance, calculating direct economic loss of the flood disaster at each moment in the disaster and indirect economic loss of the flood disaster every day after the disaster; fitting a variation curve of the flood disaster loss-time function, comparing fitting effects of different functions, and selecting the optimal fitting function and parameters thereof;
And S5, analyzing the relation between the related parameters and the disaster occurrence probability in the loss function, obtaining a relation formula of the loss function parameters and the disaster occurrence probability, and outputting the loss function.
According to one aspect of the present application, the step S1 is further:
S11, acquiring and preprocessing natural geographic data, wherein the natural geographic data comprises land utilization data, DEM data and soil data; the preprocessing process comprises format conversion and space matching, and CN values and Manning coefficients of land utilization data and soil data are determined;
step S12, acquiring and preprocessing hydrological weather data, wherein the hydrological weather data comprise design rainfall data and actual measurement rainfall data; the pretreatment process comprises time interpolation and space interpolation, and according to different reproduction periods, determining rainfall duration, a rainpeak position coefficient and rainfall intensity of the designed rainfall data;
Step S13, obtaining and preprocessing socioeconomic data, wherein the socioeconomic data comprises ponding point data, POI data, GDP grid data, department asset data, industry GDP data and input-output data; the preprocessing process comprises data screening, data integration and data standardization, and the POI data and the GDP raster data are subjected to spatial processing to form raster data.
According to one aspect of the present application, the step S2 is further:
S21, constructing a river basin one-dimensional hydrological hydrodynamic model, and determining the geometric shape of a river, the topological structure of a river network and the control rule of a sluice based on DEM data, river data and sluice data; determining input conditions of the model based on design rainfall data or actual measurement rainfall data, and determining output conditions of the model based on river channel water level data, so as to provide boundary conditions of a river channel for two-dimensional urban flood simulation;
S22, constructing an urban flood model, and adopting a one-dimensional two-dimensional hydrographic hydrodynamic coupling model; determining the geometric shape, the Manning coefficient and the CN value of the earth surface based on the DEM data, the land utilization data and the soil data; determining rainfall input conditions of the model based on the design rainfall data or the actually measured rainfall data; determining boundary conditions of the model based on the output result of the large basin model; simulating the surface water depth and flow velocity change process in the city range, and outputting hydrologic characteristic elements of grid scale;
And S23, based on the actually measured water level data, the accumulated water point data and the crop disaster area data, respectively carrying out comparative analysis on simulation results of the one-dimensional river channel model and the two-dimensional earth surface model, calculating Nash efficiency coefficients, decision coefficients and relative errors of the model, and evaluating the accuracy and applicability of the model.
According to one aspect of the present application, the step S3 is further:
Step S31, obtaining flood disaster damage assessment indexes including building assets, structure assets, equipment and tools assets, resident indoor property, flowing asset, agricultural product yield value, GDP and department yield value; converting the data into high-resolution raster data by adopting a spatialization method; dividing an indirect economic loss evaluation index system into preset industrial departments according to preset rules; constructing a direct economic loss evaluation index system and an indirect economic loss evaluation index system;
step S32, performing space distribution on the socioeconomic data of direct economic loss, wherein the step S32 comprises the following steps:
building material distribution density d B=PB/AB;PB is the total property of the building construction, and A B is the total area of the building construction;
The total value distribution density d S=PS/（ABL-AB/Lm）;ABL of the constructed material is the total area of urban and rural areas, industrial and mining areas and residential areas, and L m is the average number of floors of the building construction; p S is the total asset value of the structure;
The value distribution density d EI=PEI/AB;PEI of the equipment tool asset is the total value of the equipment tool asset;
The resident indoor property distribution density d RI,i=（∑∑NRI,i,j×Vi×Pj/100）/ARB,i;NRI,i,j represents the number of the j-th indoor property per hundred households owned by the i-th resident type; v i represents the total number of households of the town/rural residents; p j represents the value of the j-th indoor asset, and is derived from an e-commerce platform, and A RB,i is the total area of the building construction land in the urban resident/rural residential land type according to the sales weight weighted calculation of different value intervals counted by the e-commerce platform,
The liquidity distribution density d CA,i=PCA,i/AB;PCA,i is the total value of the liquidity in the ith industry;
The distribution density d A,i=GA,i/AA,i;GA,i of the yield value of the agricultural product represents the total yield value of the ith industry in agriculture, and A A,i represents the area of the land utilization type corresponding to the ith industry in agriculture;
Step S33, performing space distribution on the socioeconomic data of indirect economic loss, wherein the space distribution comprises the step of performing first industry GDP spatialization based on a unit area GDP method of land utilization; spatialization of the second and third industrial GDPs based on POI data and random forest algorithms.
According to one aspect of the present application, the step S4 is further:
Step S41, carrying out flood simulation based on the urban one-dimensional two-dimensional hydrologic hydrodynamic coupling model after the calibration parameters to obtain rasterized ponding depth data of each time step;
Step S42, constructing and adopting a loss rate calculation module, and calculating flood disaster losses at all moments based on the spread socioeconomic data; the method comprises the steps of direct economic loss evaluation and indirect economic loss evaluation, wherein the direct economic loss evaluation comprises the steps of calculating the direct economic loss of each grid unit according to loss rate curves of different types of assets by adopting a loss evaluation model based on water depth, and accumulating to obtain the direct economic loss of the whole city; the indirect economic loss evaluation comprises the steps of adopting a loss reduction and stop evaluation model, calculating initial loss of reduction and stop of each grid unit according to spatial distribution of GDP of different industries, accumulating to obtain initial loss of reduction and stop of 42 industrial departments in the whole city, further adopting a dynamic input-output model, calculating economic influence of disaster recovery of each industrial department on other industrial departments according to the initial loss of reduction and stop of production and input-output table, and calculating dynamic changes of loss of reduction and stop of production and industrial association;
The direct economic loss calculation mode is as follows:
dl= Σ sigma SA a,bαb,c,d+∑∑∑∑δb,c,dPa,b XD/365; from left to right, the subscripts that the sums match are a, b, c, d, respectively.
DL is the direct economic loss caused by flood disasters; a is flood unit number; b is an industry serial number; c is the water depth level; d is a flooding duration level; SA is asset value; alpha is the asset loss rate; delta, P and D are respectively the yield loss rate, annual yield and submerged days;
The indirect economic loss dynamic evaluation calculation mode comprises the following steps:
Initial loss of production reduction and shutdown evaluation calculation: inDL 1=∑∑∑∑γbcdGab/365; from left to right, the subscripts that the sums match are a, b, c, d, respectively.
InDL 1 is the loss of production reduction and stoppage caused by flood disasters; a. b, c and d are respectively flood unit number, industry serial number, water depth level and inundation duration level; gamma is the GDP loss rate; g is GDP of an industry;
industry association loss assessment calculation:
q（t+1）-q（t）=Δq=K{A*q（t）+e*（t）-q（t）}；
q is an abnormal degree vector representing the abnormal degree of the industrial sector relative to the normal production capacity, and is defined as a loss rate in the study; a is an incidence matrix calculated based on the input-output table; e is the disturbance vector of the final demand; k is an elasticity coefficient matrix, and represents the recovery capacity of departments after the sudden event disturbance; t is the moment;
And S43, drawing a change curve of the flood disaster loss-time function under different reproduction periods according to dynamic evaluation results of the direct economic loss and the indirect economic loss, and analyzing change characteristics and influence factors of the flood disaster loss.
According to one aspect of the present application, the step S5 is further:
s51, analyzing the relation between related parameters and disaster occurrence probability in the loss function, and discussing the change rule and influence factors of the loss function parameters;
and step S52, according to a relation formula of the loss function parameter and the disaster occurrence probability, providing an adjustment strategy for reducing the loss of the flood disaster, wherein the adjustment strategy comprises the steps of reducing loss peak value, reducing loss growth rate and improving loss growth threshold value.
According to one aspect of the application, the direct economic loss calculation process includes:
obtaining the spatial distribution of the surface water depth by using the simulation result of the urban Hong Yiwei two-dimensional hydrologic hydrodynamic coupling model;
acquiring spatial distribution of asset values by using the spatial department asset data, and performing spatial superposition analysis on the spatial distribution of asset values to obtain asset value distribution under different water depths;
calculating asset loss rate distribution under different water depths by using loss rate curves of different types of assets;
multiplying the asset loss rate distribution by the asset value distribution to obtain asset loss distribution under different water depths;
and accumulating the asset loss distribution under different water depths to obtain direct economic losses under different time.
According to one aspect of the application, the indirect economic loss calculation process includes:
obtaining the spatial distribution of the surface water depth by using the simulation result of the urban simulation model; acquiring the spatial distribution of the industrial GDP by utilizing the spatial industrial GDP data;
calculating GDP loss rate distribution under different water depths by using loss rate curves of different industries;
multiplying the GDP loss rate distribution by the GDP distribution to obtain the yield reduction loss distribution under different water depths; accumulating the loss distribution of the production reduction and stop under different water depths to obtain initial loss of the production reduction and stop;
Calculating the yield reduction loss and the industry association loss of different days after the flood disaster occurs by utilizing the input-output relation and the initial yield reduction loss of different industries;
And accumulating the production reduction loss and the industry association loss of different days to obtain indirect economic losses under different time.
According to one aspect of the application, the dynamic evaluation process of the total economic loss is also included:
acquiring dynamic changes of direct economic loss and indirect economic loss;
respectively fitting the change rules of the direct economic loss and the indirect economic loss along with time by using a loss function construction method based on the scene flood disasters;
and selecting proper fitting functions and fitting parameters according to the characteristics and the application range of the different types of loss functions, and ensuring the precision and the applicability of the loss functions.
According to another aspect of the present application, there is also provided a loss function construction system based on a scene flood disaster, including:
at least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by the processor for execution by the processor to implement the method for constructing a loss function based on a scene flooding disaster according to any one of the above technical solutions.
The method has the beneficial effects that the dynamic evaluation of the loss of the flood disasters and the fitting and selection of the loss function are combined through the proposed construction method of the loss function based on the scene flood disasters, so that the dynamic description and quantification of the loss of the flood disasters are realized. By analyzing the relation between related parameters and disaster occurrence probability in the loss function, the change rule and influence factors of the loss function parameters are analyzed, and some regulation strategies and suggestions are provided for the reduction and prevention of urban flood disaster loss. By establishing the loss function model, the method is suitable for different types of losses, flood disasters with different reproduction periods and geographic environments of different cities, and provides a unified standard and method for evaluating and comparing the losses of the flood disasters of the cities. The method solves the problem that the prior art cannot reflect the dynamic change process of flood disaster loss, and simultaneously solves the problem that the prior art lacks an indirect economic loss function of the flood disaster.
Drawings
Fig. 1 is a flow chart of the present invention.
Fig. 2 is a flowchart of step S1 of the present invention.
Fig. 3 is a flow chart of step S2 of the present invention.
Fig. 4 is a flowchart of step S3 of the present invention.
Fig. 5 is a flowchart of step S4 of the present invention.
Fig. 6 is a flowchart of step S5 of the present invention.
Detailed Description
As shown in fig. 1, a loss function construction method based on a scene flood disaster is provided, which includes the following steps:
s1, acquiring basic data of a research area and preprocessing the basic data;
Step S2, respectively constructing a one-dimensional hydrological hydrodynamic model of the river basin and a one-dimensional two-dimensional hydrological hydrodynamic coupling model of the city aiming at the river basin and the city, and calibrating model parameters;
S3, screening flood disaster damage assessment indexes, constructing a direct economic damage assessment index system and an indirect economic damage assessment index system, and performing space spreading on social economic data;
S4, carrying out flood simulation based on the urban one-dimensional two-dimensional hydrologic hydrodynamic coupling model after the calibration parameters to obtain rasterized ponding depth data of each time step; constructing and adopting a loss rate calculation module to obtain rasterized loss rate data of each time step; constructing loss evaluation modules of two time scales aiming at direct economic loss and indirect economic loss; based on the spread socioeconomic data, loss rate and industry relevance, calculating direct economic loss of the flood disaster at each moment in the disaster and indirect economic loss of the flood disaster every day after the disaster; fitting a variation curve of the flood disaster loss-time function, comparing fitting effects of different functions, and selecting the optimal fitting function and parameters thereof;
And S5, analyzing the relation between the related parameters and the disaster occurrence probability in the loss function, obtaining a relation formula of the loss function parameters and the disaster occurrence probability, and outputting the loss function.
In the embodiment, the time-varying law of the loss rate of different types of assets and industries is fitted by using flood disaster occasions with different recurrence periods, then proper functions and parameters are selected to construct a flood disaster loss-recurrence period function, meanwhile, the relation between related parameters in the loss function and disaster occurrence probability is analyzed, an adjustment strategy of the related parameters in the loss function is provided, and the effect and benefit of the adjustment strategy are evaluated. The method can effectively reflect the dynamic characteristics and the spatial characteristics of the flood disasters, improve the accuracy and the applicability of the loss function, predict the risk evolution trend of the future flood disasters, and provide basis for disaster prevention and reduction decisions.
As shown in fig. 2, according to an aspect of the present application, the step S1 is further:
S11, acquiring and preprocessing natural geographic data, wherein the natural geographic data comprises land utilization data, DEM data and soil data; the preprocessing process comprises format conversion and space matching, and CN values and Manning coefficients of land utilization data and soil data are determined;
The natural geographic data are respectively derived from the national academy of sciences resource environment science and data center, google Earth and the world soil database V1.2. The resolution and projection of these data are inconsistent, requiring format conversion, spatial matching, and data cleaning to enable analysis at the same spatial range and resolution. It is also necessary to take into account the space-time variations of land utilization data and soil data, use a chinese land utilization/coverage variation database and a chinese soil database to obtain more accurate and updated data, and use MODIS NDVI and MODIS LST to monitor variations in vegetation and surface temperature, and adjust parameters such as CN values and manning coefficients according to these variations.
Step S12, acquiring and preprocessing hydrological weather data, wherein the hydrological weather data comprise design rainfall data and actual measurement rainfall data; the pretreatment process comprises time interpolation and space interpolation, and according to different reproduction periods, determining rainfall duration, a rainpeak position coefficient and rainfall intensity of the designed rainfall data;
The hydrological data are respectively derived from a storm intensity formula and design rain standard, a Chinese meteorological data network and river channel water level data, and are derived from a Taihu river basin management agency of the water conservancy department. The temporal resolution and spatial distribution of these data are not uniform, and temporal and spatial interpolation is required to enable analysis in the same time and spatial ranges. And determining parameters such as rainfall duration, rainfall peak position coefficient, rainfall intensity and the like according to different reproduction periods. In addition, the spatial heterogeneity of rainfall is also required to be considered, the Chinese high-resolution rainfall product and the Chinese multisource fusion rainfall product are used for acquiring finer and more comprehensive rainfall data, and meanwhile, the geographic weighted regression and the kriging interpolation are used for carrying out spatial interpolation, so that the spatial variability of rainfall is considered, and the input condition of the model is more reasonable.
Step S13, obtaining and preprocessing socioeconomic data, wherein the socioeconomic data comprises ponding point data, POI data, GDP grid data, department asset data, industry GDP data and input-output data; the preprocessing process comprises data screening, data integration and data standardization, and the POI data and the GDP raster data are subjected to spatial processing to form raster data.
The social and economic data are respectively derived from a hundred-degree map, a water bureau of certain city, news reports, a Goodyear map open platform, a national academy of sciences resource science and data center, a statistical annual survey of certain city, a statistical bureau of certain city and a Chinese regional input-output table. The format and content of these data are inconsistent, and data screening, data integration and data standardization are required to enable analysis under the same unit and index. The POI data and the GDP raster data also need to be spatially processed, and the point data and the face data are converted into raster data, so that the spatial superposition analysis is conveniently carried out with the flood hydrologic characteristic data. In addition, the reliability and the effectiveness of the data are considered, the Chinese statistical annual-differentiation and Chinese urban statistical annual-differentiation are used for acquiring more authoritative and complete statistical data, and meanwhile, the data quality evaluation and data cleaning methods are used for checking and processing the data, so that errors and deviations of the data are eliminated, and the standardization and the spatial processing of the data are more accurate.
The method can acquire and preprocess natural geographic data, hydrological data and socioeconomic data, provides a complete, accurate and reliable data base for flood disaster loss evaluation, and can consider the space-time variation and the spatial heterogeneity of the data, so that the data can better reflect the actual situation of a research area and improve the adaptability and the usability of the data. The method can utilize various data sources and data processing methods to acquire and preprocess data with different types, different resolutions and different formats, solve the problems of data inconsistency, incompleteness, inaccuracy and the like, and simultaneously can utilize the spatial and standardized processing of the data to convert the data into an input format required by a model, so that the method is convenient for carrying out spatial superposition analysis with flood hydrologic characteristic data, and improves the compatibility and operability of the data.
As shown in fig. 3, according to an aspect of the present application, the step S2 is further:
S21, constructing a river basin one-dimensional hydrological hydrodynamic model, and determining the geometric shape of a river, the topological structure of a river network and the control rule of a sluice based on DEM data, river data and sluice data; determining input conditions of the model based on design rainfall data or actual measurement rainfall data, and determining output conditions of the model based on river channel water level data, so as to provide boundary conditions of a river channel for two-dimensional urban flood simulation;
The river basin large model can be a Tai lake river basin model, and a one-dimensional hydrodynamic-hydrologic coupling model is adopted to simulate the river channel water level change process in the river basin range and provide river channel water level boundary conditions for the city small model. The model is constructed by determining the geometry of the river, the topology of the river network and the control rules of the sluice based on DEM data, river data and sluice data. The model operation needs to determine the input conditions of the model based on design rainfall data or measured rainfall data, and determine the output conditions of the model based on river channel water level data. In addition, the influence of water bodies such as reservoirs, lakes and wetlands in the flow areas is considered, data of the water bodies such as reservoirs, lakes and wetlands provided by China water conservancy and hydropower science institute are used, and meanwhile, a water reservoir scheduling model and a lake wetland hydrodynamic model are used for simulating the water level change process of the water bodies, so that more perfect river water level boundary conditions are provided for urban small models.
S22, constructing an urban flood model, and adopting a one-dimensional two-dimensional hydrographic hydrodynamic coupling model; determining the geometric shape, the Manning coefficient and the CN value of the earth surface based on the DEM data, the land utilization data and the soil data; determining rainfall input conditions of the model based on the design rainfall data or the actually measured rainfall data; determining boundary conditions of the model based on the output result of the large basin model; simulating the surface water depth and flow velocity change process in the city range, and outputting hydrologic characteristic elements of grid scale;
the city model is TELEMAC-2D model, and a two-dimensional hydrodynamic-hydrologic coupling model is adopted to simulate the surface water depth and flow velocity change process in the city range and output hydrologic characteristic elements of grid scale. The model is constructed by determining parameters such as the geometric shape of the earth surface, the Manning coefficient, the CN value and the like based on DEM data, land utilization data and soil data. The model operation needs to determine the input conditions of the model based on design rainfall data or measured rainfall data, and determine the output conditions of the model based on river channel water level data. In addition, the influence of factors such as a drainage system, a green land system and a building in a city is considered, data of the drainage system provided by a certain city water service bureau is used, and an SWMM model is used for simulating the operation process of the drainage system, so that more comprehensive surface runoff parameters are provided for a city small model. It is also desirable to use the data of greenbelt systems and buildings provided by certain city planning and natural resource bureau, and to use the greenbelt system hydrologic model and building hydrodynamic model to simulate the hydrologic characteristics of greenbelt systems and buildings, providing more complex parameters such as surface geometry and manning coefficients for city small models.
And S23, based on the actually measured water level data, the accumulated water point data and the crop disaster area data, respectively carrying out comparative analysis on simulation results of the one-dimensional river channel model and the two-dimensional earth surface model, calculating Nash efficiency coefficients, decision coefficients and relative errors of the model, and evaluating the accuracy and applicability of the model.
The simulation results of the urban small model are verified and evaluated by utilizing the water accumulation point data and the hydrological site data, the size and the source of errors and uncertainty of the model are checked, and meanwhile, the sensitivity and the adaptability of the parameters of the model are evaluated and improved by utilizing methods such as error analysis, uncertainty analysis, sensitivity analysis, parameter optimization and the like, so that the evaluation index of the model is more objective and optimized.
By constructing the river basin simulation model, the river channel water level change process in the river basin range can be simulated, and the river channel water level boundary condition is provided for the city simulation model. The model not only considers the geometry of the river channel, the topological structure of the river network and the control rule of the sluice, but also considers the influence of water bodies such as reservoirs, lakes, wetlands and the like in the river basin, so that the input and output conditions of the model are more perfect and accurate, and the fidelity and reliability of the model are improved. By constructing the city simulation model, the surface water depth and flow velocity change process in the city range can be simulated, and the hydrologic characteristic elements of the grid scale are output. The model adopts a two-dimensional hydrodynamic-hydrologic coupling model, can better reflect the dynamic change and the spatial distribution of surface runoffs, and simultaneously considers the influence of factors such as a drainage system, a greenbelt system, a building and the like in the city, so that the parameters of the model are more comprehensive and complex, and the precision and the applicability of the model are improved. The accuracy and the applicability of the model can be evaluated by comparing and analyzing the measured data with the simulation result, and the sensitivity and the adaptability of the parameters of the model can be evaluated and improved by utilizing methods such as error analysis, uncertainty analysis, sensitivity analysis, parameter optimization and the like, so that the evaluation index of the model is more objective and optimized, and the credibility and the effectiveness of the model are improved.
As shown in fig. 4, according to an aspect of the present application, the step S3 is further:
Step S31, obtaining flood disaster damage assessment indexes including building assets, structure assets, equipment and tools assets, resident indoor property, flowing asset, agricultural product yield value, GDP and department yield value; converting the data into high-resolution raster data by adopting a spatialization method; dividing an indirect economic loss evaluation index system into preset industrial departments according to preset rules; constructing a direct economic loss evaluation index system and an indirect economic loss evaluation index system;
the direct economic loss evaluation index is department asset data and is derived from a Goldmap open platform, and comprises the asset quantity and the asset value of different departments. The data are point data, spatial processing is needed, the point data are converted into grid data, and spatial superposition analysis is conveniently carried out on the point data and the flood hydrologic characteristic data. The spatialization processing method is a distance-based weight distribution method, namely, the asset value of the point data is distributed to surrounding grids according to the inverse proportion of the distance, so that the asset value distributed by grids with closer distances is higher. In addition, the spatial distribution and spatial correlation of the index are considered, the spatial characteristics and spatial relation of the index are evaluated by using a spatial autocorrelation and spatial regression method, and simultaneously, the spatial processing is performed by using a spatial weight matrix and spatial partitioning method, and the spatial dependence and spatial heterogeneity of the index are considered, so that the spatial processing result is more accurate and reasonable.
Step S32, performing space distribution on the socioeconomic data of direct economic loss, wherein the step S32 comprises the following steps:
building material distribution density d B=PB/AB;PB is the total property of the building construction, and A B is the total area of the building construction;
The total value distribution density d S=PS/（ABL-AB/Lm）;ABL of the constructed material is the total area of urban and rural areas, industrial and mining areas and residential areas, and L m is the average number of floors of the building construction; p S is the total asset value of the structure;
The value distribution density d EI=PEI/AB;PEI of the equipment tool asset is the total value of the equipment tool asset;
The resident indoor property distribution density d RI,i=（∑∑NRI,i,j×Vi×Pj/100）/ARB,i;NRI,i,j represents the number of the j-th indoor property per hundred households owned by the i-th resident type; v i represents the total number of households of the town/rural residents; p j represents the value of the j-th indoor asset, and is derived from an e-commerce platform, and A RB,i is the total area of the building construction land in the urban resident/rural residential land type according to the sales weight weighted calculation of different value intervals counted by the e-commerce platform,
The liquidity distribution density d CA,i=PCA,i/AB;PCA,i is the total value of the liquidity in the ith industry;
The distribution density d A,i=GA,i/AA,i;GA,i of the yield value of the agricultural product represents the total yield value of the ith industry in agriculture, and A A,i represents the area of the land utilization type corresponding to the ith industry in agriculture;
The indirect economic loss evaluation index is industry GDP data and input-output data which are respectively derived from 'statistical annual book of certain city', statistical office of certain city and 'input-output table of China', and comprises GDP values and input-output coefficients of different industries. The data are surface data, spatial processing is needed, the surface data are converted into grid data, and spatial superposition analysis is conveniently carried out on the surface data and the flood hydrologic characteristic data. The method of the spatialization processing is an area-based weight distribution method, namely, the GDP value or input-output coefficient of the surface data is distributed into the covered grids according to the proportion of the area, so that the GDP value or input-output coefficient distributed by the grids with larger area is higher. In addition, the spatial distribution and spatial heterogeneity of the index are also required to be considered, the spatial characteristics and spatial differences of the index are evaluated by using a method of spatial distribution index and spatial dispersion index, and simultaneously, the spatialization processing is performed by using a method of spatial interpolation and spatial layering, and the spatialization result is more accurate and reasonable by considering the spatial variability and spatial layering of the index.
Step S33, performing space distribution on the socioeconomic data of indirect economic loss, wherein the space distribution comprises the step of performing first industry GDP spatialization based on a unit area GDP method of land utilization; spatialization of the second and third industrial GDPs based on POI data and random forest algorithms.
And respectively calculating direct economic loss and indirect economic loss by using a loss evaluation model based on water depth and a loss evaluation model based on input and output, accumulating to obtain total economic loss, and simultaneously considering factors such as loss rate curves and input and output relations of different types of assets and industries to ensure the accuracy and rationality of loss evaluation results.
The socioeconomic data are converted into high-resolution raster data through a spatialization method, so that spatial superposition analysis is conveniently carried out on the raster data and the flood hydrologic characteristic data, direct economic losses and indirect economic losses of different departments and industries are calculated, the influence degree and influence range of flood disasters on regional economic systems are reflected, and scientific basis is provided for post-disaster recovery reconstruction and disaster reduction planning. The spatial characteristics and spatial relations of the indexes are evaluated by taking the spatial distribution and spatial correlation of the indexes into consideration and using methods such as spatial autocorrelation, spatial regression and the like, and simultaneously, the spatialization processing is performed by using methods such as a spatial weight matrix, spatial partitioning and the like, and the spatialization result is more accurate and reasonable by taking the spatial dependence and spatial heterogeneity of the indexes into consideration. By utilizing POI data and random forest algorithm, the spatial precision of the GDP of the second industry and the third industry is improved, and the economic activity distribution in the city is better reflected. The loss evaluation model based on the water depth and the loss evaluation model based on input and output are utilized to respectively calculate the direct economic loss and the indirect economic loss, the total economic loss is obtained through accumulation, and meanwhile, factors such as loss rate curves and input and output relations of different types of assets and industries are considered, so that the accuracy and the rationality of a loss evaluation result are ensured.
As shown in fig. 5, according to an aspect of the present application, the step S4 is further:
Step S41, carrying out flood simulation based on the urban one-dimensional two-dimensional hydrologic hydrodynamic coupling model after the calibration parameters to obtain rasterized ponding depth data of each time step;
The direct economic loss calculation process includes:
Obtaining the spatial distribution of the surface water depth by using the simulation result of the urban simulation model;
acquiring spatial distribution of asset values by using the spatial department asset data, and performing spatial superposition analysis on the spatial distribution of asset values to obtain asset value distribution under different water depths;
calculating asset loss rate distribution under different water depths by using loss rate curves of different types of assets;
multiplying the asset loss rate distribution by the asset value distribution to obtain asset loss distribution under different water depths;
and accumulating the asset loss distribution under different water depths to obtain direct economic losses under different time.
Step S42, constructing and adopting a loss rate calculation module, and calculating flood disaster losses at all moments based on the spread socioeconomic data; the method comprises the steps of direct economic loss evaluation and indirect economic loss evaluation, wherein the direct economic loss evaluation comprises the steps of calculating the direct economic loss of each grid unit according to loss rate curves of different types of assets by adopting a loss evaluation model based on water depth, and accumulating to obtain the direct economic loss of the whole city; the indirect economic loss evaluation comprises the steps of adopting a loss reduction and stop evaluation model, calculating initial loss of reduction and stop of each grid unit according to spatial distribution of GDP of different industries, accumulating to obtain initial loss of reduction and stop of 42 industrial departments in the whole city, further adopting a dynamic input-output model, calculating economic influence of disaster recovery of each industrial department on other industrial departments according to the initial loss of reduction and stop of production and input-output table, and calculating dynamic changes of loss of reduction and stop of production and industrial association;
The direct economic loss calculation mode is as follows:
DL=∑∑∑∑SAa,bαb,c,d+∑∑∑∑δb,c,dPa,b×D/365；
DL is the direct economic loss caused by flood disasters; a is flood unit number; b is an industry serial number; c is the water depth level; d is a flooding duration level; SA is asset value; alpha is the asset loss rate; delta, P and D are respectively the yield loss rate, annual yield and submerged days;
The indirect economic loss dynamic evaluation calculation mode comprises the following steps:
initial loss of production reduction and shutdown evaluation calculation: inDL 1=∑∑∑∑γbcdGab/365;
InDL 1 is the loss of production reduction and stoppage caused by flood disasters; a. b, c and d are respectively flood unit number, industry serial number, water depth level and inundation duration level; gamma is the GDP loss rate; g is GDP of an industry;
industry association loss assessment calculation:
q（t+1）-q（t）=Δq=K{A*q（t）+e*（t）-q（t）}；
q is an abnormal degree vector representing the abnormal degree of the industrial sector relative to the normal production capacity, and is defined as a loss rate in the study; a is an incidence matrix calculated based on the input-output table; e is the disturbance vector of the final demand; k is an elasticity coefficient matrix, and represents the recovery capacity of departments after the sudden event disturbance; t is the moment;
the indirect economic loss calculation process includes:
Obtaining the spatial distribution of the surface water depth by using the simulation result of the urban simulation model;
Acquiring the spatial distribution of the industrial GDP by utilizing the spatial industrial GDP data;
carrying out space superposition analysis on the two to obtain industrial GDP distribution under different water depths;
Calculating industry indirect economic loss distribution under different water depths by utilizing input-output relations of different industries;
multiplying industry indirect economic loss distribution by industry GDP distribution to obtain industry loss distribution under different water depths; and accumulating the industry loss distribution under different water depths to obtain the indirect economic loss under different time.
And S43, drawing a change curve of the flood disaster loss-time function under different reproduction periods according to dynamic evaluation results of the direct economic loss and the indirect economic loss, and analyzing change characteristics and influence factors of the flood disaster loss.
Dynamic assessment process of total economic loss:
acquiring dynamic changes of direct economic loss and indirect economic loss;
Adding the direct economic loss and the indirect economic loss to obtain dynamic change of the total economic loss;
Fitting a time-dependent change rule of total economic loss by using a loss function construction method based on scene flood disasters;
and selecting proper fitting functions and fitting parameters according to the characteristics and the application range of the different types of loss functions, and ensuring the precision and the applicability of the loss functions.
And carrying out flood simulation by using the urban simulation model with the rated parameters to obtain rasterized ponding depth data of each time step, and providing basic data for subsequent loss evaluation. The method can reflect the dynamic evolution process of the flood, considers the space-time distribution characteristics of the flood, and improves the simulation precision and practicality. And constructing and adopting a loss rate calculation module, and calculating flood disaster losses at all moments, including direct economic losses and indirect economic losses, based on the spread socioeconomic data. The loss rate curves of different types of assets, input-output relations of different industries and recovery capacities of different industries can be comprehensively considered, dynamic assessment of flood disaster loss is achieved, and the influence degree and influence range of the flood disaster on socioeconomic performance are reflected. And drawing a change curve of the flood disaster loss-time function under different reproduction periods according to dynamic evaluation results of the direct economic loss and the indirect economic loss, and analyzing change characteristics and influence factors of the flood disaster loss. The method can be used for fitting the change rule of total economic loss along with time by using a loss function construction method based on the scene flood disasters, and provides basis and reference for flood disaster risk assessment and flood control and disaster reduction decision.
As shown in fig. 6, according to an aspect of the present application, the step S5 is further:
S51, analyzing the relation between related parameters and disaster occurrence probability in the loss function, and discussing the change rule and influence factors of the loss function parameters; meanwhile, a relation formula and a relation parameter between the loss function parameter and the disaster occurrence probability are established by utilizing methods such as correlation analysis, regression analysis and gray correlation analysis, and the accuracy and the rationality of the relation formula are ensured;
and step S52, according to a relation formula of the loss function parameter and the disaster occurrence probability, providing an adjustment strategy for reducing the loss of the flood disaster, wherein the adjustment strategy comprises the steps of reducing loss peak value, reducing loss growth rate and improving loss growth threshold value.
According to different control targets and control measures, the adjusting direction and the adjusting amplitude of relevant parameters in the loss function are determined, then the adjusted loss function parameters and the disaster occurrence probability are calculated by using a relation formula of the loss function parameters and the disaster occurrence probability, then the loss function is used for calculating the flood disaster loss after adjustment, finally the effect and the benefit of the adjustment strategy are evaluated, and the rationality and the effectiveness of the adjustment strategy are ensured.
By analyzing the relation between the related parameters and the disaster occurrence probability in the loss function, the change rule and the influence factor of the loss function parameters are discussed, and a relation formula and relation parameters between the loss function parameters and the disaster occurrence probability are established, so that the nonlinear growth characteristics of the flood disaster loss, and the interaction and the mutual influence between the flood disaster loss and the disaster occurrence probability can be reflected more accurately. By providing an adjusting strategy for reducing the loss of the flood disasters, including reducing loss peak value, reducing loss growth rate and improving loss growth threshold, the risk and loss of the flood disasters can be effectively reduced, the control effect and benefit are improved, and scientific basis and optimization scheme are provided for control decisions. By utilizing methods such as correlation analysis, regression analysis, gray correlation analysis and the like, a relation formula and a relation parameter between the loss function parameter and the disaster occurrence probability are established, the multiple and complexity of the loss function parameter, the uncertainty and the randomness of the disaster occurrence probability can be fully considered, and the accuracy and the rationality of the relation formula are improved.
By utilizing a relation formula of the loss function parameters and the disaster occurrence probability, an adjusting strategy for reducing the loss of the flood disaster is provided, the adjusting direction and the adjusting amplitude of the related parameters in the loss function can be determined according to different control targets and control measures, then the adjusted loss function parameters and the disaster occurrence probability as well as the adjusted loss of the flood disaster are calculated, so that the effect and the benefit of the adjusting strategy are evaluated, and the rationality and the effectiveness of the adjusting strategy are ensured.
By using the loss function, the flood disaster loss is calculated, the nonlinear growth characteristics of the flood disaster loss can be better reflected, and the interaction effect between the flood disaster loss and the disaster occurrence probability can be better reflected, so that the risk and the loss of the flood disaster can be more comprehensively estimated, and the control effect and the benefit can be improved.
According to one aspect of the application, the indirect economic loss calculation process may further be:
obtaining the spatial distribution of the surface water depth by using the simulation result of the urban simulation model; acquiring the spatial distribution of the industrial GDP by utilizing the spatial industrial GDP data;
calculating GDP loss rate distribution under different water depths by using loss rate curves of different industries;
multiplying the GDP loss rate distribution by the GDP distribution to obtain the yield reduction loss distribution under different water depths; accumulating the loss distribution of the production reduction and stop under different water depths to obtain initial loss of the production reduction and stop;
Calculating the yield reduction loss and the industry association loss of different days after the flood disaster occurs by utilizing the input-output relation and the initial yield reduction loss of different industries;
And accumulating the production reduction loss and the industry association loss of different days to obtain indirect economic losses under different time.
The dynamic evaluation process of the total economic loss can also be as follows:
acquiring dynamic changes of direct economic loss and indirect economic loss;
respectively fitting the change rules of the direct economic loss and the indirect economic loss along with time by using a loss function construction method based on the scene flood disasters;
and selecting proper fitting functions and fitting parameters according to the characteristics and the application range of the different types of loss functions, and ensuring the precision and the applicability of the loss functions.
According to another aspect of the present application, there is also provided a loss function construction system based on a scene flood disaster, including:
at least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by the processor for execution by the processor to implement the method for constructing a loss function based on a scene flooding disaster according to any one of the above technical solutions.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.
Claims (8)
1. The construction method of the loss function based on the scene flood disasters is characterized by comprising the following steps:
s1, acquiring basic data of a research area and preprocessing the basic data;
Step S2, respectively constructing a one-dimensional hydrological hydrodynamic model of the river basin and a one-dimensional two-dimensional hydrological hydrodynamic coupling model of the city aiming at the river basin and the city, and calibrating model parameters;
S3, screening flood disaster damage assessment indexes, constructing a direct economic damage assessment index system and an indirect economic damage assessment index system, and performing space spreading on social economic data;
S4, carrying out flood simulation based on the urban one-dimensional two-dimensional hydrologic hydrodynamic coupling model after the calibration parameters to obtain rasterized ponding depth data of each time step; constructing and adopting a loss rate calculation module to obtain rasterized loss rate data of each time step; constructing loss evaluation modules of two time scales aiming at direct economic loss and indirect economic loss; based on the spread socioeconomic data, loss rate and industry relevance, calculating direct economic loss of the flood disaster at each moment in the disaster and indirect economic loss of the flood disaster every day after the disaster; fitting a variation curve of the flood disaster loss-time function, comparing fitting effects of different functions, and selecting the optimal fitting function and parameters thereof;
S5, analyzing the relation between the related parameters and the disaster occurrence probability in the loss function, obtaining a relation formula of the loss function parameters and the disaster occurrence probability, and outputting the loss function;
The step S4 is further:
Step S41, carrying out flood simulation based on the urban one-dimensional two-dimensional hydrologic hydrodynamic coupling model after the calibration parameters to obtain rasterized ponding depth data of each time step;
Step S42, constructing and adopting a loss rate calculation module, and calculating flood disaster losses at all moments based on the spread socioeconomic data; the method comprises the steps of direct economic loss evaluation and indirect economic loss evaluation, wherein the direct economic loss evaluation comprises the steps of calculating the direct economic loss of each grid unit according to loss rate curves of different types of assets by adopting a loss evaluation model based on water depth, and accumulating to obtain the direct economic loss of the whole city; the indirect economic loss evaluation comprises the steps of adopting a loss reduction and stop evaluation model, calculating initial loss of reduction and stop of each grid unit according to spatial distribution of GDP of different industries, accumulating to obtain initial loss of reduction and stop of 42 industrial departments in the whole city, further adopting a dynamic input-output model, calculating economic influence of disaster recovery of each industrial department on other industrial departments according to the initial loss of reduction and stop of production and input-output table, and calculating dynamic changes of loss of reduction and stop of production and industrial association;
The direct economic loss calculation mode is as follows:
DL=∑∑∑∑SAa,bαb,c,d+∑∑∑∑δb,c,dPa,b×D/365；
DL is the direct economic loss caused by flood disasters; a is flood unit number; b is an industry serial number; c is the water depth level; d is a flooding duration level; SA is asset value; alpha is the asset loss rate; delta, P and D are respectively the yield loss rate, annual yield and submerged days;
The indirect economic loss dynamic evaluation calculation mode comprises the following steps:
initial loss of production reduction and shutdown evaluation calculation: inDL 1=∑∑∑∑γbcdGab/365;
InDL 1 is the loss of production reduction and stoppage caused by flood disasters; a. b, c and d are respectively flood unit number, industry serial number, water depth level and inundation duration level; gamma is the GDP loss rate; g is GDP of an industry;
industry association loss assessment calculation:
q（t+1）-q（t）=Δq=K{ A* q（t）+e*（t）-q（t）}；
q is an abnormal degree vector, representing the abnormal degree of the industry department relative to the normal production capacity, and is defined as a loss rate; a is an incidence matrix calculated based on the input-output table; e is the disturbance vector of the final demand; k is an elasticity coefficient matrix, and represents the recovery capacity of departments after the sudden event disturbance; t is the moment;
Step S43, according to dynamic evaluation results of direct economic loss and indirect economic loss, drawing a change curve of flood disaster loss-time function under different reproduction periods, and analyzing change characteristics and influence factors of the flood disaster loss;
The step S5 is further:
s51, analyzing the relation between related parameters and disaster occurrence probability in the loss function, and discussing the change rule and influence factors of the loss function parameters;
and step S52, according to a relation formula of the loss function parameter and the disaster occurrence probability, providing an adjustment strategy for reducing the loss of the flood disaster, wherein the adjustment strategy comprises the steps of reducing loss peak value, reducing loss growth rate and improving loss growth threshold value.
2. The method for constructing a loss function based on a scene flood disaster as claimed in claim 1, wherein the step S1 is further:
S11, acquiring and preprocessing natural geographic data, wherein the natural geographic data comprises land utilization data, DEM data and soil data; the preprocessing process comprises format conversion and space matching, and CN values and Manning coefficients of land utilization data and soil data are determined;
step S12, acquiring and preprocessing hydrological weather data, wherein the hydrological weather data comprise design rainfall data and actual measurement rainfall data; the pretreatment process comprises time interpolation and space interpolation, and according to different reproduction periods, determining rainfall duration, a rainpeak position coefficient and rainfall intensity of the designed rainfall data;
Step S13, obtaining and preprocessing socioeconomic data, wherein the socioeconomic data comprises ponding point data, POI data, GDP grid data, department asset data, industry GDP data and input-output data; the preprocessing process comprises data screening, data integration and data standardization, and the POI data and the GDP raster data are subjected to spatial processing to form raster data.
3. The method for constructing a loss function based on a scene flood disaster as claimed in claim 2, wherein the step S2 is further:
S21, constructing a river basin one-dimensional hydrological hydrodynamic model, and determining the geometric shape of a river, the topological structure of a river network and the control rule of a sluice based on DEM data, river data and sluice data; determining input conditions of the model based on design rainfall data or actual measurement rainfall data, and determining output conditions of the model based on river channel water level data, so as to provide boundary conditions of a river channel for two-dimensional urban flood simulation;
S22, constructing a one-dimensional two-dimensional hydrologic hydrodynamic coupling model of the city; determining the geometric shape, the Manning coefficient and the CN value of the earth surface based on the DEM data, the land utilization data and the soil data; determining rainfall input conditions of the model based on the design rainfall data or the actually measured rainfall data; determining boundary conditions of the model based on the output result of the large basin model; simulating the surface water depth and flow velocity change process in the city range, and outputting hydrologic characteristic elements of grid scale;
and S23, based on the actually measured water level data, the accumulated water point data and the crop disaster area data, respectively carrying out comparative analysis on simulation results of the one-dimensional hydrologic hydrodynamic model of the river basin and the one-dimensional two-dimensional hydrologic hydrodynamic coupling model of the city, calculating Nash efficiency coefficients, decision coefficients and relative errors of the model, and evaluating the accuracy and applicability of the model.
4. The method for constructing a loss function based on a scene flood disaster as claimed in claim 3, wherein the step S3 is further:
Step S31, obtaining flood disaster damage assessment indexes including building assets, structure assets, equipment and tools assets, resident indoor property, flowing asset, agricultural product yield value, GDP and department yield value; converting the data into high-resolution raster data by adopting a spatialization method; dividing an indirect economic loss evaluation index system into preset industrial departments according to preset rules; constructing a direct economic loss evaluation index system and an indirect economic loss evaluation index system;
step S32, performing space distribution on the socioeconomic data of direct economic loss, wherein the step S32 comprises the following steps:
building material distribution density d B=PB/AB;PB is the total property of the building construction, and A B is the total area of the building construction;
The total value distribution density d S=PS/（ABL-AB/Lm）;ABL of the constructed material is the total area of urban and rural areas, industrial and mining areas and residential areas, and L m is the average number of floors of the building construction; p S is the total asset value of the structure;
The value distribution density d EI=PEI/AB;PEI of the equipment tool asset is the total value of the equipment tool asset;
The resident indoor property distribution density d RI,i=（∑∑NRI,i,j×Vi×Pj/100）/ARB,i;NRI,i,j represents the number of the j-th indoor property per hundred households owned by the i-th resident type; v i represents the total number of households of the town/rural residents; p j represents the value of the j-th indoor asset, and is derived from an e-commerce platform, and A RB,i is the total area of the building construction land in the urban resident/rural residential land type according to the sales weight weighted calculation of different value intervals counted by the e-commerce platform,
The liquidity distribution density d CA,i=PCA,i/AB;PCA,i is the total value of the liquidity in the ith industry;
The distribution density d A,i=GA,i/AA,i;GA,i of the yield value of the agricultural product represents the total yield value of the ith industry in agriculture, and A A,i represents the area of the land utilization type corresponding to the ith industry in agriculture;
Step S33, performing space distribution on the socioeconomic data of indirect economic loss, wherein the space distribution comprises the step of performing first industry GDP spatialization based on a unit area GDP method of land utilization; spatialization of the second and third industrial GDPs based on POI data and random forest algorithms.
5. The method for constructing a loss function based on a scene flood disaster as claimed in claim 4, wherein the direct economic loss calculation process comprises:
obtaining the spatial distribution of the surface water depth by using the simulation result of the urban Hong Yiwei two-dimensional hydrologic hydrodynamic coupling model;
acquiring spatial distribution of asset values by using the spatial department asset data, and performing spatial superposition analysis on the spatial distribution of asset values to obtain asset value distribution under different water depths;
calculating asset loss rate distribution under different water depths by using loss rate curves of different types of assets;
multiplying the asset loss rate distribution by the asset value distribution to obtain asset loss distribution under different water depths;
and accumulating the asset loss distribution under different water depths to obtain direct economic losses under different time.
6. The method for constructing a loss function based on a scene flood disaster according to claim 5, wherein the indirect economic loss calculation process comprises:
obtaining the spatial distribution of the surface water depth by using the simulation result of the urban simulation model; acquiring the spatial distribution of the industrial GDP by utilizing the spatial industrial GDP data;
calculating GDP loss rate distribution under different water depths by using loss rate curves of different industries;
multiplying the GDP loss rate distribution by the GDP distribution to obtain the yield reduction loss distribution under different water depths; accumulating the loss distribution of the production reduction and stop under different water depths to obtain initial loss of the production reduction and stop;
Calculating the yield reduction loss and the industry association loss of different days after the flood disaster occurs by utilizing the input-output relation and the initial yield reduction loss of different industries;
And accumulating the production reduction loss and the industry association loss of different days to obtain indirect economic losses under different time.
7. The method for constructing a loss function based on a scene flood disaster according to claim 5, further comprising a dynamic evaluation process of total economic loss:
acquiring dynamic changes of direct economic loss and indirect economic loss;
respectively fitting the change rules of the direct economic loss and the indirect economic loss along with time by using a loss function construction method based on the scene flood disasters;
and selecting proper fitting functions and fitting parameters according to the characteristics and the application range of the different types of loss functions, and ensuring the precision and the applicability of the loss functions.
8. A loss function construction system based on a scene flood disaster, comprising:
at least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by the processor for execution by the processor to implement the scene flood disaster-based loss function construction method of any one of claims 1 to 7.
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