A flood disaster assessment method and system integrating precipitation regime and time elements

Abstract

The present application relates to the technical field of hydrological data processing, and proposes a flood disaster assessment method and system integrating precipitation situation and time elements, which comprises: obtaining precipitation sample data of a region to be assessed, and performing standardization processing on the precipitation sample data; taking the precipitation sample data after standardization processing as a disaster-causing element, and constructing a Gompertz function family based on time-varying characteristics linearly changing with time to obtain a flood disaster assessment model for representing the relationship between precipitation situation and flood disaster loss; performing parameter fitting on the flood disaster assessment model, and selecting a function with the highest parameter fitting convergence rate from the Gompertz function family to perform flood disaster assessment, thereby obtaining a flood disaster assessment result. The present application takes the precipitation sample data after standardization processing as a disaster-causing element, and introduces time-varying characteristics linearly changing with time, thereby completing model construction integrating precipitation situation and time elements, which is conducive to dynamic analysis of flood disaster loss and effectively improves the accuracy of the assessment result.

Description

Technical Field

[0001] This invention relates to the field of hydrological data processing technology, and more specifically, to a method and system for assessing flood disasters by integrating precipitation conditions and time factors. Background Technology

[0002] Floods are natural disasters that have a significant impact on society and the ecological environment, and my country is one of the countries most prone to floods. Flood risk is mainly characterized by hazard, vulnerability, and exposure, representing the impact of the disaster-bearing entity's socio-economic conditions, disaster prevention capabilities, and disaster intensity on disaster risk. Engineering and non-engineering measures are the main means of coping with floods and can significantly reduce the losses caused by them. On the one hand, with population and economic growth, the potential losses from floods may be even greater; on the other hand, socio-economic development will also improve urban disaster prevention and mitigation capabilities, and the rational application of engineering and non-engineering measures can address and reduce the risks of floods. Therefore, in the context of rapid urbanization and economic development, the assessment of flood losses is a multi-dimensional and complex issue.

[0003] A systematic assessment of flood disaster losses can measure the relationship between the input and output of disaster prevention and mitigation, thus providing a basis for flood control engineering construction. Currently, a commonly used method is to construct a Gompertz function to characterize the relationship between disaster-causing factors and disaster losses. However, the commonly used Gompertz function lacks consideration of precipitation patterns, which is a key meteorological factor leading to flood disasters. Furthermore, due to socio-economic development and the construction of flood control projects, the losses caused by the same level of disaster vary from year to year. A single Gompertz function cannot reflect time-varying characteristics, thus the accuracy of the resulting flood disaster loss assessment needs improvement. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, which lacks consideration of precipitation conditions and fails to reflect time-varying characteristics when conducting systematic assessments of flood disaster losses by constructing Gompertz functions, resulting in lower accuracy of assessment results, this invention provides a flood disaster assessment method and system that integrates precipitation conditions and time elements.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A flood disaster assessment method that integrates precipitation conditions and time factors includes the following steps:

[0007] Obtain precipitation sample data from the area to be evaluated and standardize them;

[0008] Using standardized precipitation sample data as disaster-causing factors, and constructing a family of Gompertz functions based on time-varying characteristics that change linearly with time, a flood disaster assessment model is obtained to characterize the relationship between precipitation conditions and flood disaster losses.

[0009] The flood disaster assessment model is fitted with parameters, and the function with the highest parameter fitting convergence rate is selected from the Gompertz function family to conduct flood disaster assessment, thus obtaining the flood disaster assessment results.

[0010] Furthermore, this invention proposes a flood disaster assessment system that integrates precipitation conditions and time factors, applying the flood disaster assessment method proposed in this invention. The system includes:

[0011] The data acquisition module is used to acquire precipitation sample data of the area to be evaluated;

[0012] The standardization processing module is used to standardize the collected precipitation sample data.

[0013] The flood disaster assessment model construction module is used to construct a family of Gompertz functions based on standardized precipitation sample data as disaster-causing factors and time-varying characteristics that change linearly with time, so as to obtain a flood disaster assessment model that characterizes the relationship between precipitation situation and flood disaster loss.

[0014] The evaluation module is used to fit the parameters of the flood disaster assessment model, select the function with the highest parameter fitting convergence rate from the Gompertz function family for flood disaster assessment, and output the flood disaster assessment results.

[0015] Furthermore, the present invention also proposes a computer device including a memory and a processor, wherein the memory stores computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor causes the processor to perform the steps of the flood disaster assessment method proposed in the present invention.

[0016] Furthermore, the present invention also proposes a storage medium storing computer-readable instructions, wherein the computer-readable instructions, when executed by a processor, implement the steps of the flood disaster assessment method proposed in the present invention.

[0017] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0018] This invention uses standardized precipitation sample data as disaster-causing factors and introduces time-varying characteristics that change linearly with time, thereby completing the construction of a Gompertz function family that integrates precipitation situation and time factors, which is beneficial for dynamic analysis of flood disaster losses.

[0019] This invention also improves the accuracy of assessment results by fitting parameters to a flood disaster assessment model and selecting a suitable disaster loss assessment model based on the convergence rate. Attached Figure Description

[0020] Figure 1 This is a flowchart of the flood disaster assessment method based on integrated precipitation conditions and time factors in Example 1.

[0021] Figure 2 This is a graph showing the effect of different parameter values ​​on the shape of the Gompertz function.

[0022] Figure 3 This is a schematic diagram showing the fitting convergence rate of the eight forms in the Gompertz function family.

[0023] Figure 4 This is a schematic diagram comparing the a0 parameter with historical flood disaster losses in different cases.

[0024] Figure 5 This is a schematic diagram showing the flood disaster assessment results of the Gompertz function for region A.

[0025] Figure 6 This is a schematic diagram illustrating the flood disaster assessment results of the Gompertz function for region B.

[0026] Figure 7 This is a schematic diagram showing the flood disaster assessment results of the Gompertz function for region C.

[0027] Figure 8 This is a diagram illustrating the architecture of the flood disaster assessment system that integrates precipitation conditions and time factors, as described in Example 3. Detailed Implementation

[0028] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent; it is understandable that some well-known descriptions in the drawings may be omitted in order to better illustrate this embodiment.

[0029] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] Example 1

[0031] This embodiment proposes a flood disaster assessment method that integrates precipitation conditions and time factors, such as... Figure 1 The diagram shown is a flowchart of the flood disaster assessment method based on the integrated precipitation situation and time elements in this embodiment.

[0032] The flood disaster assessment method based on integrated precipitation conditions and time factors proposed in this embodiment includes the following steps:

[0033] S1. Obtain precipitation sample data of the area to be evaluated and standardize it.

[0034] S2. Using standardized precipitation sample data as disaster-causing factors, and constructing a family of Gompertz functions based on time-varying characteristics that change linearly with time, a flood disaster assessment model is obtained to characterize the relationship between precipitation conditions and flood disaster losses.

[0035] S3. Perform parameter fitting on the flood disaster assessment model, and select the function with the highest parameter fitting convergence rate from the Gompertz function family to conduct flood disaster assessment and obtain the flood disaster assessment results.

[0036] In this embodiment, the Gompertz function family is constructed using standardized precipitation sample data as the disaster-causing factor, and time-varying characteristics that change linearly with time are introduced. This completes the mathematical modeling of flood disaster losses based on the comprehensive precipitation situation and time factors, which is beneficial for dynamic analysis of flood disaster losses. Furthermore, this embodiment improves the accuracy of the assessment results by fitting parameters to the flood disaster assessment model and selecting a suitable disaster loss assessment model based on the convergence rate.

[0037] As an example, the precipitation sample data in this embodiment includes precipitation data and disaster loss data for the area to be evaluated.

[0038] In one optional embodiment, the standardization process for the precipitation sample data includes using a parameterless empirical cumulative distribution function, a gamma distribution function, and / or using maximum likelihood estimation through parameter fitting.

[0039] Furthermore, in an optional embodiment, the precipitation sample data is standardized using a cumulative distribution function. Its expression is:

[0040]

[0041] Wherein, SPI represents the standardized precipitation index corresponding to precipitation sample data p; CDF represents the inverse function of the cumulative distribution function of the standard normal distribution. P (·) represents the cumulative distribution function obtained by fitting the precipitation sample.

[0042] In this embodiment, the standardized precipitation index (SPI) is used to characterize the precipitation situation at different times. The larger the SPI value, the more precipitation there is, and the smaller the SPI value, the less precipitation there is.

[0043] In an optional embodiment, in step S2, the standardized precipitation sample data is used as the disaster-causing factor, and the Gompertz function family constructed based on the time-varying characteristics that change linearly with time is represented as follows:

[0044]

[0045] Where, d 000 (SPI,t),d 100 (SPI,t),…,d 111 (SPI,t) are functions used to assess flood damage; t represents the time element; a0, a1, b0, b1, c0, and c1 are time-varying characteristic parameters in the Gompertz family of functions.

[0046] In this embodiment, the function d 000 (SPI,t) is a function that does not consider the influence of time, indicating that the overall level of disaster loss, the rate of change of disaster loss, and the threshold of disaster-causing factors do not have time-varying characteristics.

[0047] function d 100 (SPI,t) is a function that takes into account the time-varying characteristics of parameter a, representing the overall level of disaster loss that changes linearly over time.

[0048] function d 010 (SPI,t) is a function that takes into account the time-varying characteristics of parameter b, representing the rate of change of disaster loss as a linear change over time.

[0049] function d 001 (SPI,t) is a function that takes into account the time-varying characteristics of parameter c, representing that the threshold of disaster-causing factors changes linearly with time.

[0050] function d 110 (SPI,t) is a function that takes into account the time-varying characteristics of parameters a and b, representing the overall level of disaster loss and the rate of change of disaster loss as a linear relationship with time.

[0051] function d 101 (SPI,t) is a function that takes into account the time-varying characteristics of parameters a and c, representing the linear change of the overall level of disaster loss and the threshold of disaster-causing factors over time.

[0052] function d 011 (SPI,t) is a function that takes into account the time-varying characteristics of parameters b and c, representing the rate of change of disaster loss and the linear change of the threshold of disaster-causing factors over time.

[0053] function d 111(SPI,t) is a function that takes into account the time-varying characteristics of parameters a, b, and c, representing the overall level of disaster loss, the rate of change of disaster loss, and the threshold of disaster-causing factors as they change linearly over time.

[0054] Where a = a0 + a1t, b = b0 + b1t, c = c0 + c1t.

[0055] In this embodiment, the Gompertz function family uses the standardized precipitation index SPI obtained in step S1 as the disaster-causing factor, i.e., x = SPI, to characterize the relationship between precipitation conditions and flood disaster losses.

[0056] As an example, the Gompertz function in this embodiment takes the following form:

[0057] d(x)=aexp(-exp(-b(xc)))

[0058] Where d(x) represents the flood disaster loss; a, b and c are parameters in the Gompertz function, where a represents the overall level of disaster loss; b represents the rate of change of disaster loss, comprehensively representing the vulnerability of the carrier; and c represents the threshold of the disaster-causing factor, representing the turning point where the marginal loss of flood disaster changes from increasing to decreasing.

[0059] In the above Gompertz function form, when using the static analysis method, it characterizes the relationship between flood disaster loss d(x) and disaster-causing factor x under the overall level a of specific disaster loss, the rate of change of disaster loss b, and the threshold c of disaster-causing factor, without considering the changes of parameters a, b, and c over time.

[0060] In order to consider the relationship between flood disaster losses and time, this embodiment designs one or more of the parameters a, b, and c to vary with time, that is, let a = a0 + a1t, b = b0 + b1t, c = c0 + c1t, and use the standardized precipitation index SPI obtained in step S1 as the disaster-causing factor, that is, x = SPI, so as to characterize the relationship between precipitation situation and flood disaster losses, and obtain a family of flood disaster loss functions that consider different time-varying conditions.

[0061] Furthermore, in an optional embodiment, the time-varying characteristic parameters in the flood disaster assessment model are estimated using a nonlinear least squares method, including the following steps:

[0062] (1) Construct the objective function L; its expression is as follows:

[0063]

[0064] Wherein d(SPI i ,t i;θ) represents a form in the Gompertz family of functions; SPI i and t i Let represent the SPI and time element of the i-th precipitation sample; θ represents the time-varying characteristic parameter set to be estimated, i.e., θ∈[a0,a1,b0,b1,c0,c1]; y i Let N represent the flood damage loss of the i-th precipitation sample, and N represent the total number of precipitation samples.

[0065] (2) Optimize the time-varying characteristic parameter set θ by minimizing the objective function L; its expression is as follows:

[0066]

[0067] Where, θ opt This represents the estimated value of the time-varying characteristic parameter set θ.

[0068] Furthermore, in an optional embodiment, the Levenberg-Marquardt algorithm is used for optimization when the objective function L is minimized.

[0069] Further, in an optional embodiment, parameter fitting of the flood disaster assessment model includes: fitting the flood disaster assessment model using the precipitation sample data, and evaluating the convergence result of any functional form through case studies; wherein, for any functional form, its parameter fitting convergence rate r is calculated, and its expression is as follows:

[0070]

[0071] Where, n c n represents the number of cases where the parameter fit successfully converges. t This indicates the total number of cases.

[0072] In this embodiment, the Gompertz function family is fitted using collected precipitation sample data. Then, the convergence performance of eight function forms in the family is evaluated through case studies, and further selection is performed based on the convergence rate. A higher convergence rate r indicates better convergence of the function, and when the convergence rate r equals 100%, it means that the function converges in all cases.

[0073] In this embodiment, a function form with high convergence rate for parameter fitting is selected from the Gompertz function family to further conduct flood disaster loss analysis, which can further improve the accuracy of the systematic assessment results.

[0074] Example 2

[0075] This embodiment applies the flood disaster assessment method based on the comprehensive precipitation situation and time elements proposed in Embodiment 1, and performs simulation based on precipitation data and flood disaster-affected population and direct economic loss data from 2006 to 2021.

[0076] The specific steps of this embodiment are as follows:

[0077] Step 1: Obtain various precipitation sample data for disaster loss assessment.

[0078] This step uses the open_dataset function from the Python third-party library Xarray to read the precipitation dataset from 2006 to 2021, and uses the read_csv function from Pandas to read the statistical data on the population affected by floods and the direct economic losses, which are stored in the variables prec, ploss, and eloss, respectively.

[0079] Step 2: Perform data modeling and analysis on the data collected in S1.

[0080] First, a Gamma distribution is fitted based on the precipitation data, and the parameters are estimated using the maximum likelihood method. Then, the standardized precipitation index SPI is calculated based on the cumulative distribution function of the fitted Gamma distribution and stored in the variable spi.

[0081] Step 3: Construct the Gompertz function family to obtain a flood disaster assessment model that characterizes the relationship between precipitation conditions and flood disaster losses.

[0082] In this step, NumPy is primarily used to perform the relevant mathematical calculations and encapsulate them into functions. Specifically, based on the variables spi, ploss, and eloss obtained in the previous steps, SciPy is used to perform parameter estimation for the Gompertz family of functions.

[0083] The disaster loss is represented by the affected population and direct economic loss, respectively. The parameters of the Gompertz function are estimated and stored in dictionaries named para_p and para_e, respectively. The dictionary keys are the function names from the Gompertz family of function expressions: 000, 100, 010, 001, 110, 101, 011, and 111, distinguishing eight different function forms. For example... Figure 2 The image shows the effect of different parameter values ​​on the shape of the Gompertz function. Figure 2 The parts (a), (b), and (c) in the figure correspond to the effects of different values ​​of parameters a, b, and c on the shape of the Gompertz function.

[0084] Step 4: Conduct an applicability assessment of the Gompertz function family.

[0085] First, assess the convergence rate of the parameters in the flood disaster assessment model.

[0086] like Figure 3 The diagram shows the convergence rates of eight different forms of the Gompertz function family. Figure 3 Parts (a) and (b) in the figure are schematic diagrams illustrating the convergence rates of the Gompertz function family for the affected population and direct economic losses, respectively. As shown in the figure, the function d... 100 (SPI,t) and d 010 The high convergence rate of the parameters (SPI,t) indicates good convergence properties.

[0087] Further analysis of parameter rationality. This step involves analyzing the rationality of the parameters by plotting a violin diagram of parameter a0 and historical flood event losses, as shown below. Figure 4 The diagram shows a comparison of the a0 parameter with historical flood disaster losses in different cases. Figure 4 Parts (a) and (b) in the text represent the function d. 100 Violin plots in (SPI,t) showing the relationship between parameter a0 and the affected population and direct economic loss, respectively. Figure 4 Parts (c) and (d) in the text represent the function d. 010 Violin plots in (SPI,t) showing the relationship between parameter a0 and the affected population and direct economic losses.

[0088] As can be seen from the graph, the function d 100 The parameter a0 in (SPI,t) may exceed the affected population and direct economic losses by several orders of magnitude, which does not conform to the physical meaning of the parameter. In summary, the function d... 010 (SPI,t) shows a good fit in this example.

[0089] Step 5: Select the target Gompertz function based on the applicability assessment results of Step 4, and use it to further conduct flood disaster loss assessment in three typical regions.

[0090] Typical regions A, B, and C were selected, and flood disaster loss assessment results based on the Gompertz function family were plotted using Matplotlib, as shown below. Figure 5 , Figure 6 and Figure 7 As shown. Among them, Figure 5 Parts (a) and (b) in the diagram represent the results of the Gompertz function's assessment of the flood disaster in region A, including the affected population and direct economic losses. Figure 6Parts (a) and (b) in the diagram represent the results of the Gompertz function's assessment of the flood disaster in region B, including the affected population and direct economic losses. Figure 7 Parts (a) and (b) in the diagram represent the results of the Gompertz function's assessment of the flood disaster in region C, including the affected population and direct economic losses.

[0091] As shown in the figure, the Gompertz function family proposed in this invention can reflect the changes in disaster losses due to precipitation conditions. That is, the greater the precipitation, the greater the disaster losses, and the increase in disaster losses gradually slows down after the precipitation reaches a certain level.

[0092] The Gompertz function family proposed in this invention can dynamically reflect the changes in disaster losses over time. That is, under the same precipitation conditions, the number of people affected by disasters in region A has decreased in recent years, while the direct economic losses have increased. In regions B and C, the number of people affected by disasters and the direct economic losses have decreased significantly in recent years.

[0093] Example 3

[0094] This embodiment proposes a flood disaster assessment system that integrates precipitation conditions and time factors, applying the flood disaster assessment method proposed in Embodiment 1. For example... Figure 8 The diagram shown is an architecture diagram of the flood disaster assessment system in this embodiment.

[0095] The flood disaster assessment system based on integrated precipitation and time factors proposed in this embodiment includes:

[0096] The data acquisition module is used to obtain precipitation sample data of the area to be evaluated.

[0097] The standardization processing module is used to standardize the collected precipitation sample data.

[0098] The flood disaster assessment model construction module is used to construct a family of Gompertz functions based on standardized precipitation sample data as disaster-causing factors and time-varying characteristics that change linearly over time, thereby obtaining a flood disaster assessment model that characterizes the relationship between precipitation conditions and flood disaster losses.

[0099] The evaluation module is used to fit the parameters of the flood disaster assessment model, select the function with the highest parameter fitting convergence rate from the Gompertz function family for flood disaster assessment, and output the flood disaster assessment results.

[0100] It is understood that the system in this embodiment corresponds to the method in Embodiment 1 above, and the options in Embodiment 1 above are also applicable to this embodiment, so they will not be described again here.

[0101] Example 4

[0102] This embodiment proposes a computer device, including a memory and a processor. The memory stores computer-readable instructions, which, when executed by the processor, cause the processor to perform the steps of the flood disaster assessment method proposed in Embodiment 1.

[0103] Example 5

[0104] This embodiment proposes a storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, they implement the steps of the flood disaster assessment method proposed in Embodiment 1.

[0105] By way of example, the storage medium includes, but is not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks, and other media capable of storing program code.

[0106] By way of example, the instructions, programs, code sets, or instruction sets may be implemented using conventional programming languages.

[0107] By way of example, the processor includes, but is not limited to, smartphones, personal computers, servers, network devices, etc., for performing all or part of the steps of the extreme precipitation event analysis method described in Example 1.

[0108] The terminology used in the accompanying drawings is for illustrative purposes only and should not be construed as limiting the scope of this patent.

[0109] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing flood disasters by integrating precipitation patterns and temporal factors, characterized in that, Includes the following steps: Obtain precipitation sample data from the area to be evaluated and standardize them; Using standardized precipitation sample data as disaster-causing factors, and constructing a family of Gompertz functions based on time-varying characteristics that change linearly with time, a flood disaster assessment model is obtained to characterize the relationship between precipitation conditions and flood disaster losses. The flood disaster assessment model is fitted with parameters, and the function with the highest parameter fitting convergence rate is selected from the Gompertz function family to conduct flood disaster assessment, thus obtaining the flood disaster assessment results.

2. The flood disaster assessment method according to claim 1, characterized in that, The standardization process for the precipitation sample data includes: using a parameterless empirical cumulative distribution function, a gamma distribution function, and / or using maximum likelihood estimation through parameter fitting.

3. The flood disaster assessment method according to claim 2, characterized in that, During the standardization process of the precipitation sample data, the cumulative distribution function is used for standardization. Its expression is: Wherein, SPI represents the standardized precipitation index corresponding to precipitation sample data p; CDF represents the inverse function of the cumulative distribution function of the standard normal distribution. P (·) represents the cumulative distribution function obtained by fitting the precipitation sample.

4. The flood disaster assessment method according to claim 1, characterized in that, The flood disaster assessment model includes: Where, d 000 (SPI,t),d 100 (SPI,t),…,d 111 (SPI,t) are functions used to assess flood damage; t represents the time element; SPI is standardized precipitation sample data, representing the disaster-causing factor; a0, a1, b0, b1, c0, and c1 are time-varying characteristic parameters in the Gompertz family of functions. function d 000 (SPI,t) is a function that does not consider the influence of time, indicating that the overall level of disaster loss, the rate of change of disaster loss, and the threshold of disaster-causing factors do not have time-varying characteristics. function d 100 (SPI,t) is a function that takes into account the time-varying characteristics of parameter a, representing the overall level of disaster loss changing linearly over time; function d 010 (SPI,t) is a function that takes into account the time-varying characteristics of parameter b, representing the rate of change of disaster loss as a linear change over time; function d 001 (SPI,t) is a function that takes into account the time-varying characteristics of parameter c, representing that the threshold of disaster-causing factors changes linearly with time. function d 110 (SPI,t) is a function that takes into account the time-varying characteristics of parameters a and b, representing the linear change of the overall level of disaster loss and the rate of change of disaster loss over time. function d 101 (SPI,t) is a function that takes into account the time-varying characteristics of parameters a and c, representing the linear change of the overall level of disaster loss and the threshold of disaster-causing factors over time. function d 011 (SPI,t) is a function that takes into account the time-varying characteristics of parameters b and c, representing the linear change of the rate of change of disaster loss and the threshold of disaster-causing factors over time. function d 111 (SPI,t) is a function that takes into account the time-varying characteristics of parameters a, b, and c, representing the overall level of disaster loss, the rate of change of disaster loss, and the threshold of disaster-causing factors that change linearly over time. Where a = a0 + a1t, b = b0 + b1t, c = c0 + c1t.

5. The flood disaster assessment method according to claim 4, characterized in that, The time-varying characteristic parameters in the flood disaster assessment model are estimated using the nonlinear least squares method, including the following steps: Construct the objective function L; its expression is as follows: Wherein d(SPI i ,t i ;θ) represents a form in the Gompertz family of functions; SPI i and t i Let represent the SPI and time element of the i-th precipitation sample; θ represents the time-varying characteristic parameter set to be estimated, i.e., θ∈[a0,a1,b0,b1,c0,c1]; y i Let N represent the flood damage loss of the i-th precipitation sample, and N represent the total number of precipitation samples. The optimization is performed with the objective function L as the goal, and the values ​​of the time-varying characteristic parameter set θ are estimated; its expression is as follows: Where, θ opt This represents the estimated value of the time-varying characteristic parameter set θ.

6. The flood disaster assessment method according to claim 5, characterized in that, When optimizing to minimize the objective function L, the Levenberg-Marquardt algorithm is used.

7. The flood disaster assessment method according to claim 4, characterized in that, The parameters of the flood disaster assessment model are fitted, including: The flood disaster assessment model is fitted using the precipitation sample data, and the convergence result of any functional form is evaluated through case studies. For any functional form, the parameter fitting convergence rate r is calculated, and its expression is as follows: Where, n c n represents the number of cases where the parameter fit successfully converges. t This indicates the total number of cases.

8. A flood disaster assessment system integrating precipitation conditions and time elements, employing the flood disaster assessment method integrating precipitation conditions and time elements as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire precipitation sample data of the area to be evaluated; The standardization processing module is used to standardize the collected precipitation sample data. The flood disaster assessment model construction module is used to construct a family of Gompertz functions based on standardized precipitation sample data as disaster-causing factors and time-varying characteristics that change linearly with time, so as to obtain a flood disaster assessment model that characterizes the relationship between precipitation situation and flood disaster loss. The evaluation module is used to fit the parameters of the flood disaster assessment model, select the function with the highest parameter fitting convergence rate from the Gompertz function family for flood disaster assessment, and output the flood disaster assessment results.

9. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, characterized in that, When the computer-readable instructions are executed by the processor, the processor performs the steps of the flood disaster assessment method as described in any one of claims 1 to 7.

10. A storage medium having computer-readable instructions stored thereon, characterized in that, When the computer-readable instructions are executed by a processor, they implement the steps of the flood disaster assessment method as described in any one of claims 1 to 7.

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