Emergency material warehouse layout method and system based on historical disasters and regional characteristics

Abstract

The application discloses an emergency material warehouse layout method and system based on historical disasters and regional characteristics, and belongs to the technical field of emergency management and spatial decision; the method comprises the following steps: normalizing parameters in a demand point set, and calculating comprehensive weights; using kernel density estimation to calculate a demand point spatial distribution density set, and selecting a candidate warehouse set; taking minimizing weighted distance, minimizing construction cost and maximizing coverage weight as targets, a multi-objective optimization model is constructed; the fitness of a current candidate material warehouse scheme is calculated; s schemes are selected from the candidate material warehouse scheme group, and an individual with the optimal fitness is selected until the scale reaches M; two parent schemes are randomly selected, and a distribution factor is calculated; the parent schemes are mutated to obtain child schemes; parent and child scheme groups are combined, the first M optimal individuals are reserved to obtain a new scheme group; if the number of iterations does not reach the maximum number of iterations and the fitness does not converge, iteration is continued; and decision support is provided for scientific storage and rapid deployment of emergency materials.

Description

Technical Field

[0001] This invention belongs to the field of emergency management and spatial decision-making technology, specifically involving a method and system for the layout of emergency material depots based on historical disasters and regional characteristics. Background Technology

[0002] The layout of emergency supply depots is a key element in improving disaster relief efficiency, and its rationality directly impacts the effectiveness of material allocation during the critical post-disaster relief period. Traditional layout methods often rely on balanced distribution across administrative regions or experience-based judgments, which struggle to address the temporal and spatial uncertainties of disasters and the dynamic changes in regional characteristics, leading to insufficient material allocation or resource waste in high-risk areas. With accelerating urbanization and increasingly complex disaster chains, there is an urgent need to establish a scientific and dynamic emergency supply depot layout methodology to provide precise decision support for disaster risk management.

[0003] To address the issues of reliance on administrative distribution and manual experience in traditional emergency material depot layout methods, several solutions have been proposed by academic and industrial communities both domestically and internationally. Current technical solutions in this field primarily focus on specific disaster scenarios and optimization objectives. Patent application number 202410241895.1 proposes an optimized layout method for emergency material reserve depots in response to sudden water pollution events. This method identifies risk sources by establishing an industry risk level database and constructs an optimization model with dual objective functions. The first objective function minimizes the number of reserve depots, while the second objective function integrates construction benefits and distance factors. This method employs a phased solution strategy, first determining the range of possible reserve depot numbers, and then iteratively optimizing. Patent application number 202210811969.1 designs a layout method for chemical industrial parks. By establishing a node network and assigning distance coefficients, it constructs a rescue path network, calculates the minimum distance between nodes, proposes the concept of characteristic distance, and determines the optimal location using a sorting algorithm. Patent application number 202410962222.5 proposes a general site selection method that optimizes the layout of existing storage facilities by constructing a mathematical model and using a genetic algorithm to solve it. While these methods have achieved some success in different application scenarios, they still have significant limitations.

[0004] Existing technologies suffer from two key limitations: first, they lack a single data dimension, with most solutions focusing only on geographical distance or static risk levels, failing to consider the characteristic distribution of disaster data and urban-rural planning; second, they lack dynamic adaptability, as fixed-parameter optimization models cannot adapt to the spatiotemporal development trends of cities and the coordination issues between different resource demand points. These problems lead to situations where existing systems are prone to delays in material allocation and imbalances in resource distribution in real disaster scenarios, necessitating the development of intelligent layout technologies that integrate multi-source spatiotemporal data with adaptive optimization algorithms. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method and system for the layout of emergency material warehouses based on historical disasters and regional characteristics, thereby solving the problems in the prior art.

[0006] The objective of this invention can be achieved through the following technical solutions: The emergency supplies depot layout method based on historical disasters and regional characteristics includes the following steps: S1, to obtain a set of demand points by statistically analyzing the demand points for emergency supplies within the region; S2, standardizes the parameters in the demand point set and calculates the comprehensive weight; S3, based on comprehensive weights, uses kernel density estimation to calculate the set of spatial distribution density of demand points, and selects the top M points from the local maxima in the set as the candidate library set; S4. Based on the candidate library set, construct a multi-objective optimization model with the objectives of minimizing weighted distance, minimizing construction cost, and maximizing coverage weight. S5, Generate a group of candidate material warehouse schemes, and calculate the fitness of the current candidate material warehouse schemes based on the objective function in the multi-objective optimization model; S6. Select s solutions from the candidate material pool solution group, and select the individual with the best fitness as the parent solution, until the size of the parent solution group reaches M. S7. Randomly select two parent schemes from the parent scheme group, calculate the distribution factor, and form a crossover offspring scheme group. S8, mutate the parent scheme to obtain the child scheme, forming a group of mutated child schemes; S9: Merge the parent and child scheme groups, retain the top M best individuals to obtain a new scheme group; if the number of iterations has not reached the maximum number of generations and the fitness has not converged, return to S6 to continue iterating.

[0007] Furthermore, the set of demand points D = { d 1, d 2,…, d N};in: In the formula, i Indicates the first i One demand point, E i This indicates the number of disasters that have occurred in the past five years. I i Indicates the density of critical facilities. P i This indicates the proportion of the population aged 65 and over. x i , y iThese represent the coordinates of the required points.

[0008] Furthermore, the calculation process for the comprehensive weight is as follows: in, i Indicates the first i One demand point, For comprehensive weighting, This represents the influence coefficient, which satisfies... ; These represent the number of disasters in the past five years, the density of critical facilities, and the proportion of the population aged 65 and above, respectively. max() and min() represent taking the maximum and minimum values, respectively.

[0009] Furthermore, the set of spatial distribution density of the demand points ,in The calculation expression is: In the formula, For the first j Spatial distribution density of each demand point For the number of demand points, for x Standard deviation of axial coordinates for y Standard deviation of the axial coordinates; , The coordinates of the required point.

[0010] Furthermore, the objective function of the multi-objective optimization model is: in, , , These represent the weighted distance, construction cost, and coverage weight, respectively, with Cost representing the construction cost function. R Let 1 represent the coverage radius, and 1 represent the indicator function that outputs 1 if the condition is met. d ij Indicate demand points d i Arrive at the supply point c j Euclidean distance; K This indicates the number of material warehouses built under budget constraints.

[0011] Furthermore, the candidate material warehouse scheme group The fitness The calculation formula is: in, wj The relative coefficients of the objective function satisfy , L C represents the number of material warehouse plans. i = { c 1, c 2,…, cK} is the first in the scheme group i A material warehouse plan.

[0012] Furthermore, the distribution factor The formula for calculation is: The process of generating sub-schemes based on the distribution factor is as follows: in, The distance distribution index between parents and offspring is represented by the index. For random numbers, { x ', y '} represents the coordinate group of the sub-scheme, { x , y} represents the coordinate group of the parent scheme.

[0013] An emergency supplies depot layout system based on historical disasters and regional characteristics includes: Collection Acquisition Module: Collects statistics on emergency material demand points within the region to obtain a set of demand points; Weight calculation module: Standardizes the parameters in the set of demand points and calculates the comprehensive weight; Candidate set selection module: Based on comprehensive weights, kernel density estimation is used to calculate the spatial distribution density set of demand points, and the top M points from the local maxima in the set are selected as the candidate pool set; Model building module: Based on the candidate library set, construct a multi-objective optimization model with the objectives of minimizing weighted distance, minimizing construction cost, and maximizing coverage weight; Fitness calculation module: Generates a group of candidate material warehouse solutions and calculates the fitness of the current candidate material warehouse solution based on the objective function in the multi-objective optimization model; Parent solution group construction module: Select s solutions from the candidate material library solution group, select the individual with the best fitness as the parent solution, until the size of the parent solution group reaches M. Distribution factor calculation module: Randomly select two parent schemes from the parent scheme group, calculate the distribution factor, and form a cross-off offspring scheme group; The offspring scheme group construction module mutates the parent scheme to obtain offspring schemes, forming a mutated offspring scheme group. Merge optimization module: Merge the parent and child solution groups, retain the top M best individuals to obtain a new solution group; if the number of iterations has not reached the maximum number of generations and the fitness has not converged, return to S6 to continue iterating.

[0014] A computer storage medium storing a readable program that, when executed, instructs a computing device to perform the emergency supplies depot layout method based on historical disasters and regional characteristics as described above.

[0015] An electronic device includes: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform operations corresponding to the emergency material warehouse layout method based on historical disasters and regional characteristics described above.

[0016] The beneficial effects of this invention are: 1. This invention analyzes historical disaster data and regional multidimensional features in real time, dynamically calculates the comprehensive weight of each demand point, generates candidate locations for material warehouses based on spatial optimization algorithms, and optimizes the layout scheme of material warehouses through a multi-objective model, thereby providing decision support for the scientific storage and rapid allocation of emergency materials.

[0017] 2. This invention constructs a dynamic weighting model by integrating multi-dimensional features such as historical disaster frequency, critical infrastructure density, and the proportion of the elderly population. Compared with traditional static weight allocation methods, this model can respond to changes in regional risk in real time, thereby obtaining higher priority during the optimization process.

[0018] 3. This invention employs spatial distribution density estimation to extract candidate material repository locations from the distribution of demand points, overcoming the limitations of traditional layout methods that rely on fixed grids or administrative centers. Based on the fundamental idea of ​​genetic algorithms, it simultaneously optimizes three objectives: response time, construction cost, and coverage efficiency. Furthermore, it balances global exploration and local development capabilities from an iterative perspective, providing cost-effective alternatives. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is the basic data table of the demand points selected in Example 1; Figure 2 Generate a table for the candidate material database; Figure 3 This is a table showing the iterative process of the genetic algorithm. Figure 4 Assign a detailed table to the demand points; Figure 5 This is a flowchart of the emergency supplies warehouse layout method of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1 like Figure 5 As shown, the emergency material depot layout method based on historical disasters and regional characteristics includes the following steps: S1, to obtain a set of demand points by statistically analyzing the demand points for emergency supplies within the region; For the set of demand points D={ d 1, d 2,…, d N}; the data within d i for: (1) in, i Indicates the first i One demand point, E i This indicates the number of disasters that have occurred in the past five years. I i This indicates the density of critical facilities such as hospitals, schools, and nursing homes. P i This indicates the proportion of the population aged 65 and over. x i , y i These represent the coordinates of the demand points; In this embodiment, the demand point set data is as follows: Figure 1As shown in the table, the data presents multidimensional characteristics of some demand points, including disaster risk indicators, facility density, proportion of elderly population, spatial coordinates, and comprehensive weights. It can be seen that there are significant spatial differences in disaster risk in this region, uneven distribution of infrastructure, and regional differences in population age structure.

[0023] S2, standardizes the parameters in the demand point set and calculates the comprehensive weight; For the set of demand points, data normalization is used to standardize the parameters to obtain... And calculate the comprehensive weight according to equation (5); (2) (3) (4) (5) in, i Indicates the first i One demand point, For comprehensive weighting, This represents the influence coefficient, which satisfies... ; These represent the number of disasters in the past five years, the density of critical infrastructure, and the proportion of the population aged 65 and above, respectively. `max()` and `min()` represent taking the maximum and minimum values, respectively. In this embodiment, the comprehensive weight calculation result is as follows: Figure 1 As shown in the seventh column of the table.

[0024] S3, based on the comprehensive weight calculated in S2, uses kernel density estimation to calculate the set of spatial distribution density of demand points, and selects the top M points from the local maxima in the set as the candidate library set; Demand point spatial distribution density set ,in The calculation expression is: (6) In the formula, For the first j Spatial distribution density of each demand point For the number of demand points, for x Standard deviation of axial coordinates for y Standard deviation of the axial coordinates; , The coordinates of the required point. N The number of demand points; Selected candidate library set ; In this embodiment, the generated candidate material library group is as follows: Figure 2 As shown in the table below.

[0025] S4, based on the candidate library set, minimize the weighted distance. f 1. Minimize construction costs f 2 and maximizing coverage weight f 3. Construct a multi-objective optimization model with the objective of 3. The objective function of the multi-objective optimization model is: (7) (8) (9) in, , , These represent the weighted distance, construction cost, and coverage weight, respectively, with Cost representing the construction cost function. R This represents the coverage radius (in this embodiment, the coverage radius is selected as 30km), and 1 represents an indicator function that outputs 1 when the condition is met. d ij Indicate demand points d i Arrive at the supply point c j Euclidean distance; K This indicates the number of material warehouses built under budget constraints.

[0026] S5, generate L Group candidate material storage schemes and calculate the fitness of the current candidate material storage scheme based on the objective function in the multi-objective optimization model; The material warehouse scheme group is defined as Fitness is defined as fitness The calculation formula is: (10) Where wj is the relative coefficient of the objective function, satisfying ; In this embodiment, the calculated initial fitness is as follows: Figure 3 As shown in the first column of the table.

[0027] S6. Select s solutions from the candidate resource pool solution group, and choose the individual with the best fitness as the parent solution, until the size of the parent solution group reaches 6. M indivual; The optimal method for selecting individuals is: (11) in, This represents the individual with the best fitness. Random index; In this embodiment, the number of randomly selected items s is chosen to be 3.

[0028] S7. Randomly select two parent schemes P1 and P2 from the parent scheme group and calculate the distribution factor. Generate sub-solutions O1 and O2; thus forming a cross-sub-solution group. (12) (13) in, The distance distribution index between parents and offspring is represented by the index. For random numbers, { x ', y '} represents the coordinate group of the sub-scheme, { x , y} represents the coordinate group of the parent scheme.

[0029] S8, for parent scheme P j With probability p m Mutation is performed to obtain sub-scheme O. j This constitutes a group of variant offspring schemes; The process of mutating the parent scheme Pj with probability pm includes: (14) in, The value represents the variation perturbation, which conforms to the set of demand points. x Normal distribution of the standard deviation of the axial coordinates. To represent the variation perturbation value, it conforms to the set of demand points. y Normal distribution of the standard deviation of the axial coordinates; S9, merges the parent and offspring scheme groups (the crossover offspring scheme group of S7 and the variant offspring scheme group of S8), retaining the previous ones. M The optimal individuals yield a new group of solutions; if the number of iterations does not reach the maximum number of generations G. max If the fitness does not converge, return to S6 to continue the iteration; The expression for obtaining the new scheme group is as follows: (15) in, Indicates the preceding M A new group of solutions composed of optimal individuals; For the parent generation of schemes, C represents the child scheme group, and C is the material warehouse scheme in the scheme group, which is used to calculate fitness and sort and select the optimal scheme. Furthermore, the criteria for fitness convergence are: Where G represents the current iteration round number, M Let be the number of solutions in the solution group. F i For the first i The suitability of each option This is the convergence threshold; In this embodiment, the fitness change and the coordinates of the resource depot during the iteration process are as follows: Figure 3 As shown, the details of the material warehouses allocated to the demand points are as follows: Figure 4 As shown, the genetic algorithm used in this invention can effectively drive the continuous optimization of the resource warehouse layout scheme. The optimal fitness decreased from 1.25 to 0.68 within 100 generations, and the coordinates of the five resource warehouses showed regular fine-tuning with iteration. The final layout scheme achieved coverage of all demand points, verifying the significant effect of the method of this invention in optimizing convergence and global search capability. Based on a similar inventive concept, embodiments of the present invention also provide a computer storage medium storing a readable program that, when run by a processor, can execute the above-described method for emergency material depot layout based on historical disasters and regional characteristics.

[0030] Based on a similar inventive concept, this invention provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described emergency material warehouse layout method based on historical disasters and regional characteristics.

[0031] Based on a similar inventive concept, embodiments of the present invention also provide a computer program product, including computer instructions, which instruct a computing device to perform the operations corresponding to the above-described emergency material warehouse layout method based on historical disasters and regional characteristics.

[0032] Example 2 An emergency supplies depot layout system based on historical disasters and regional characteristics includes: Collection Acquisition Module: Collects statistics on emergency material demand points within the region to obtain a set of demand points; Weight calculation module: Standardizes the parameters in the set of demand points and calculates the comprehensive weight; Candidate set selection module: Based on comprehensive weights, kernel density estimation is used to calculate the spatial distribution density set of demand points, and the top M points from the local maxima in the set are selected as the candidate pool set; Model building module: Based on the candidate library set, construct a multi-objective optimization model with the objectives of minimizing weighted distance, minimizing construction cost, and maximizing coverage weight; Fitness calculation module: Generates a group of candidate material warehouse solutions and calculates the fitness of the current candidate material warehouse solution based on the objective function in the multi-objective optimization model; Parent solution group construction module: Select s solutions from the candidate material library solution group, select the individual with the best fitness as the parent solution, until the size of the parent solution group reaches M. Distribution factor calculation module: Randomly selects two parent schemes from the parent scheme group and calculates the distribution factor. , generate sub-schemes, and form a cross-sub-scheme group; The offspring scheme group construction module mutates the parent scheme to obtain offspring schemes, forming a mutated offspring scheme group. Merge optimization module: Merge the parent and child solution groups, retain the top M best individuals to obtain a new solution group; if the number of iterations has not reached the maximum number of generations G. max If the fitness does not converge, return to S6 to continue the iteration.

[0033] The methods of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses the code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for performing the methods shown herein.

[0034] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for the layout of emergency material depots based on historical disasters and regional characteristics, characterized in that, Includes the following steps: S1, to obtain a set of demand points by statistically analyzing the demand points for emergency supplies within the region; S2, standardizes the parameters in the demand point set and calculates the comprehensive weight; S3, based on comprehensive weights, uses kernel density estimation to calculate the set of spatial distribution density of demand points, and selects the top M points from the local maxima in the set as the candidate library set; S4. Based on the candidate library set, construct a multi-objective optimization model with the objectives of minimizing weighted distance, minimizing construction cost, and maximizing coverage weight. S5, Generate a group of candidate material warehouse schemes, and calculate the fitness of the current candidate material warehouse schemes based on the objective function in the multi-objective optimization model; S6. Select s solutions from the candidate material pool solution group, and select the individual with the best fitness as the parent solution, until the size of the parent solution group reaches M. S7. Randomly select two parent schemes from the parent scheme group, calculate the distribution factor, and form a crossover offspring scheme group. S8, mutate the parent scheme to obtain the child scheme, forming a group of mutated child schemes; S9: Merge the parent and child scheme groups, retain the top M best individuals to obtain a new scheme group; if the number of iterations has not reached the maximum number of generations and the fitness has not converged, return to S6 to continue iterating.

2. The emergency material depot layout method based on historical disasters and regional characteristics according to claim 1, characterized in that, The set of demand points D = { d 1, d 2,…, d N };in: In the formula, i Indicates the first i One demand point, E i This indicates the number of disasters that have occurred in the past five years. I i Indicates the density of critical facilities. P i This indicates the proportion of the population aged 65 and over. x i , y i These represent the coordinates of the required points.

3. The emergency material depot layout method based on historical disasters and regional characteristics according to claim 2, characterized in that, The calculation process for the comprehensive weight is as follows: in, i Indicates the first i One demand point, For comprehensive weighting, This represents the influence coefficient, which satisfies... ; These represent the number of disasters in the past five years, the density of critical facilities, and the proportion of the population aged 65 and above, respectively. max() and min() represent taking the maximum and minimum values, respectively.

4. The emergency material depot layout method based on historical disasters and regional characteristics according to claim 3, characterized in that, The set of spatial distribution density of demand points ,in The calculation expression is: In the formula, For the first j Spatial distribution density of each demand point For the number of demand points, for x Standard deviation of axial coordinates for y Standard deviation of the axial coordinates; , The coordinates of the required point.

5. The emergency material depot layout method based on historical disasters and regional characteristics according to claim 4, characterized in that, The objective function of the multi-objective optimization model is: in, , , These represent the weighted distance, construction cost, and coverage weight, respectively, with Cost representing the construction cost function. R Let 1 represent the coverage radius, and 1 represent the indicator function that outputs 1 if the condition is met. d ij Indicate demand points d i Arrive at the supply point c j Euclidean distance; K This indicates the number of material warehouses built under budget constraints.

6. The emergency material depot layout method based on historical disasters and regional characteristics according to claim 5, characterized in that, The candidate material warehouse scheme group The fitness The calculation formula is: in, wj The relative coefficients of the objective function satisfy , L C represents the number of material warehouse plans. i = { c 1, c 2,…, cK } is the first in the scheme group i A material warehouse plan.

7. The emergency material depot layout method based on historical disasters and regional characteristics according to claim 1, characterized in that, The distribution factor The formula for calculation is: The process of generating sub-schemes based on the distribution factor is as follows: in, The distance distribution index between parents and offspring is represented by the index. For random numbers, { x ', y '} represents the coordinate group of the sub-scheme, { x , y } represents the coordinate group of the parent scheme.

8. An emergency material depot layout system based on historical disasters and regional characteristics, characterized in that: Specifically, it includes: Collection Acquisition Module: Collects statistics on emergency material demand points within the region to obtain a set of demand points; Weight calculation module: Standardizes the parameters in the set of demand points and calculates the comprehensive weight; Candidate set selection module: Based on comprehensive weights, kernel density estimation is used to calculate the spatial distribution density set of demand points, and the top M points from the local maxima in the set are selected as the candidate pool set; Model building module: Based on the candidate library set, construct a multi-objective optimization model with the objectives of minimizing weighted distance, minimizing construction cost, and maximizing coverage weight; Fitness calculation module: Generates a group of candidate material warehouse solutions and calculates the fitness of the current candidate material warehouse solution based on the objective function in the multi-objective optimization model; Parent solution group construction module: Select s solutions from the candidate material library solution group, select the individual with the best fitness as the parent solution, until the size of the parent solution group reaches M. Distribution factor calculation module: Randomly select two parent schemes from the parent scheme group, calculate the distribution factor, and form a cross-off offspring scheme group; The offspring scheme group construction module mutates the parent scheme to obtain offspring schemes, forming a mutated offspring scheme group. Merge optimization module: Merge the parent and child solution groups, retain the top M best individuals to obtain a new solution group; if the number of iterations has not reached the maximum number of generations and the fitness has not converged, return to S6 to continue iterating.

9. A computer storage medium storing a readable program, characterized in that, When the program is running, it can instruct the computing device to execute the emergency material warehouse layout method based on historical disasters and regional characteristics as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the emergency material warehouse layout method based on historical disasters and regional characteristics as described in any one of claims 1-7.

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