Method, system, device and storage medium for calculating debris flow scale-frequency based on disaster history data

Abstract

The present invention discloses a method, system, device and storage medium for calculating the scale-frequency of debris flows based on historical disaster data, and the method includes: obtaining historical debris flow disaster data; obtaining a relationship between the scale-frequency of debris flows. This application combines Bayesian theory, and through computational analysis, can effectively evaluate the probability distribution of debris flows of different scales, and provide a scientific basis for the prevention and mitigation of debris flows; according to the proposed debris flow scale-frequency calculation method, the scale distribution of debris flows that occur once in a hundred years, once in fifty years, and once in thirty years can be obtained, and the relevant departments can prepare disaster response measures in advance based on the calculation results; the debris flow scale-frequency calculation method of this application fully considers the scale and frequency of debris flows, helps to reveal the temporal and spatial distribution laws of debris flow activities, and provides technical support for regional debris flow risk management and early warning system construction.

Description

Technical Field

[0001] The present invention belongs to the technical field of disaster management and extreme event analysis, and in particular relates to a method, system, device and storage medium for calculating the scale-frequency of debris flows based on disaster history data. Background Art

[0002] Mountain torrents and debris flows are a serious natural disaster that poses a huge threat to human life, property, and the ecological environment. The scale and frequency of debris flows are important indicators for assessing their danger, and are also the key to disaster prevention and mitigation. However, traditional debris flow scale-frequency calculation methods often rely on empirical formulas or theoretical models. These methods have certain limitations in application, such as the subjectivity of parameter selection, limitations in model applicability, and scarcity of data. They cannot well reveal the spatiotemporal distribution of debris flow activities, and cannot provide technical support for regional debris flow risk management and early warning system construction. Summary of the invention

[0003] The purpose of the present invention is to provide a debris flow scale-frequency calculation method, system, device and storage medium based on disaster history data to solve the problem that the existing technology cannot well reveal the temporal and spatial distribution laws of debris flow activities and cannot provide technical support for regional debris flow risk management and early warning system construction.

[0004] The embodiment of the present application is implemented by providing a method for calculating the scale-frequency of debris flow based on disaster history data, including:

[0005] Obtain historical data on debris flow disasters;

[0006] The relationship between the scale and frequency of debris flow is obtained. , where:

[0007] For time;

[0008] is the scale of the selected debris flow;

[0009] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0010] is the scale parameter of the generalized Pareto distribution;

[0011] is the shape parameter of the generalized Pareto distribution;

[0012] is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows.

[0013] In some embodiments, the debris flow disaster history data includes the name of the debris flow ditch, the time when the debris flow ditch occurred, the location of the debris flow ditch, and the amount of debris flow.

[0014] In some embodiments, the generalized Pareto distribution is used to fit the distribution of debris flow discharge exceeding the threshold. Assuming that N debris flow events occur in a debris flow ditch, the debris flow scales are recorded as , , ,···, , assuming that the scale of debris flow is a random event, the scale of debris flow exceeding scale M obeys the generalized Pareto distribution, and the probability density function of the generalized Pareto distribution is:

[0015] ;

[0016] The distribution function of the generalized Pareto distribution is

[0017] ;

[0018] In the formula, the parameters take Hill Determine by graph method, specifically: , , ,···, Arrange them in descending order into a new sequence , Hill The statistic is defined as , by point The curve formed is called Hill Figure, the data corresponding to the horizontal coordinate of the starting point of the stable area in the figure Threshold ;

[0019] parameter and Bayesian methods are used for estimation, including:

[0020] For parameters and , select the first prior distribution;

[0021] Construct the first likelihood function, ;

[0022] Calculate the first posterior distribution, according to Bayes' theorem, , The parameters are and The first prior distribution of , then the first posterior distribution is expressed as: ;

[0023] Take the first posterior distribution mean or median as a parameter and An estimated value of

[0024] M is the selected debris flow scale;

[0025] X It is a collection of debris flow gully scale;

[0026] For the i The scale of the debris flow gully;

[0027] , , ,···, are the scales of each debris flow gully;

[0028] are the scales of each debris flow gully, in descending order;

[0029] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0030] is the scale parameter of the generalized Pareto distribution;

[0031] is the shape parameter of the generalized Pareto distribution;

[0032] is the scale parameter The first prior distribution of

[0033] is the shape parameter The first prior distribution of .

[0034] In some embodiments, the first prior distribution comprises a normal distribution or a gamma distribution.

[0035] In some embodiments, based on the debris flow disaster history data, the number of debris flow occurrences each year is counted to obtain a set , using the Poisson process to fit the over-scale M The frequency of debris flows λ , assuming that the number of debris flow events follows a Poisson process, Representative T The scale of debris flow in the year exceeds M The number of times is n The probability of is the intensity parameter of the Poisson distribution, that is, the frequency of debris flow, time Exceeding the scale The probability distribution density of debris flow events is:

[0036] ;

[0037] In the formula, the parameter λ is estimated using the Bayesian method, and the estimation methods include:

[0038] For each parameter λ, a second prior distribution is selected;

[0039] Construct the second likelihood function, ;

[0040] Calculate the second posterior distribution. According to Bayes' theorem, is the second prior distribution of parameter λ, then the second posterior distribution is: ;

[0041] Take the mean or median of the second posterior distribution as the estimated value of parameter λ;

[0042] T For time;

[0043] is the selected debris flow scale;

[0044] is the number of debris flows occurring each year;

[0045] Expressed as time Exceeding the scale the number of debris flows;

[0046] is the number of debris flows;

[0047] is the intensity parameter of the Poisson process, and is also the frequency of debris flow;

[0048] is the strength parameter The second prior distribution of

[0049] is the strength parameter The second posterior distribution of .

[0050] In some embodiments, the second prior distribution comprises a normal distribution or a gamma distribution.

[0051] In some embodiments, it is assumed that the debris flow first occurs when the size exceeds The time is ,make ,but The probability density distribution function and expectation of are:

[0052] ;

[0053] ;

[0054] Depend on Available, time Exceeding the scale The expectation of debris flow events is:

[0055] ;

[0056] because and Equivalently, the two are combined to get: ,Will Bring in The relationship between debris flow scale and frequency is obtained:

[0057] , where:

[0058] For time;

[0059] is the scale of the selected debris flow;

[0060] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0061] is the scale parameter of the generalized Pareto distribution;

[0062] is the shape parameter of the generalized Pareto distribution;

[0063] k The time when the first debris flow scale exceeds M occurs;

[0064] is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows.

[0065] Accordingly, the embodiment of the present application also provides a debris flow scale-frequency calculation system based on disaster history data, including:

[0066] A debris flow disaster history data acquisition module is used to obtain debris flow disaster history data;

[0067] The debris flow scale-frequency relationship acquisition module is used to obtain the debris flow scale-frequency relationship. The debris flow scale-frequency relationship is: , where:

[0068] For time;

[0069] is the scale of the selected debris flow;

[0070] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0071] is the scale parameter of the generalized Pareto distribution;

[0072] is the shape parameter of the generalized Pareto distribution;

[0073] is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows.

[0074] Accordingly, an embodiment of the present application also provides a computer device, including a storage device and a processor, wherein the storage device stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0075] Accordingly, an embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the processor executes the steps of the above method.

[0076] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0077] In this application:

[0078] Generalized Pareto distribution is the generalized Pareto distribution;

[0079] The Poisson process is a Poisson process.

[0080] This application collects debris flow disaster history data based on published literature or reports, uses Bayesian statistics to obtain the parameters of generalized Pareto distribution and Poisson distribution, and obtains the scale-frequency relationship of debris flow based on the derived scale-frequency formula. This application combines Bayesian theory, and through calculation and analysis, it can effectively evaluate the probability distribution of debris flows of different scales, and provide a scientific basis for debris flow disaster prevention and mitigation; according to the proposed debris flow scale-frequency calculation method, the scale distribution of debris flows that occur once in a hundred years, once in fifty years, and once in thirty years can be obtained, and the relevant departments can prepare disaster response measures in advance based on the calculation results; the debris flow scale-frequency calculation method of this application fully considers the scale and frequency of debris flows, which helps to reveal the temporal and spatial distribution laws of debris flow activities, and provides technical support for regional debris flow risk management and early warning system construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A debris flow volume distribution diagram provided by an embodiment of the present invention;

[0082] Figure 2 A debris flow occurrence year-frequency graph provided by an embodiment of the present invention;

[0083] Figure 3 A posterior distribution diagram of the Bayesian inference parameter σ provided in an embodiment of the present invention;

[0084] Figure 4 A posterior distribution diagram of the Bayesian inference parameter ξ provided in an embodiment of the present invention;

[0085] Figure 5 Bayesian inference parameters provided by the embodiment of the present invention λ The posterior distribution of ;

[0086] Figure 6 A debris flow scale-frequency relationship diagram provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0088] The technical solution of this application is as follows:

[0089] In a first aspect, the present application provides a method for calculating the scale-frequency of debris flow based on disaster history data, including:

[0090] S01. Obtain the historical data of debris flow disasters;

[0091] S02. Obtain the relationship between the scale and frequency of debris flow. The relationship between the scale and frequency of debris flow is: , where:

[0092] For time;

[0093] is the scale of the selected debris flow;

[0094] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0095] is the scale parameter of the generalized Pareto distribution;

[0096] is the shape parameter of the generalized Pareto distribution;

[0097] is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows.

[0098] This application collects debris flow disaster history data based on published literature or reports, uses Bayesian statistics to obtain the parameters of generalized Pareto distribution and Poisson distribution, and obtains the scale-frequency relationship of debris flow based on the derived scale-frequency formula. This application combines Bayesian theory, and through calculation and analysis, it can effectively evaluate the probability distribution of debris flows of different scales, and provide a scientific basis for debris flow disaster prevention and mitigation; according to the proposed debris flow scale-frequency calculation method, the scale distribution of debris flows that occur once in a hundred years, once in fifty years, and once in thirty years can be obtained, and the relevant departments can prepare disaster response measures in advance based on the calculation results; the debris flow scale-frequency calculation method of this application fully considers the scale and frequency of debris flows, which helps to reveal the temporal and spatial distribution laws of debris flow activities, and provides technical support for regional debris flow risk management and early warning system construction.

[0099] In the S01:

[0100] In some embodiments, the debris flow disaster history data includes the name of the debris flow ditch, the time when the debris flow ditch occurred, the location of the debris flow ditch, and the amount of debris flow.

[0101] It is understandable that the debris flow disaster history data is obtained based on literature or survey reports.

[0102] Furthermore, the generalized Pareto distribution is used to fit the distribution of debris flow discharge exceeding the threshold. Assuming that N debris flow events occur in a debris flow gully, the debris flow scales are recorded as , , ,···, , assuming that the scale of debris flow is a random event, the scale of debris flow exceeding scale M obeys the generalized Pareto distribution, and the probability density function of the generalized Pareto distribution is:

[0103] ;

[0104] The distribution function of the generalized Pareto distribution is

[0105] ;

[0106] In the formula, the parameters take Hill Determine by graph method, specifically: , , ,···, Arrange them in descending order into a new sequence , Hill The statistic is defined as , by point The curve formed is called Hill Figure, the data corresponding to the horizontal coordinate of the starting point of the stable area in the figure Threshold ;

[0107] parameter and Bayesian methods are used for estimation, including:

[0108] S011. For parameters and , select the first prior distribution;

[0109] S012, constructing the first likelihood function, ;

[0110] S013. Calculate the first posterior distribution. According to Bayes' theorem, , The parameters are and The first prior distribution of , then the first posterior distribution is expressed as: ;

[0111] S014. Take the first posterior distribution mean or median as a parameter and An estimated value of

[0112] M is the selected debris flow scale;

[0113] X It is a collection of debris flow gully scale;

[0114] For the i The scale of the debris flow gully;

[0115] , , ,···, are the scales of each debris flow gully;

[0116] are the scales of each debris flow gully, in descending order;

[0117] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0118] is the scale parameter of the generalized Pareto distribution;

[0119] is the shape parameter of the generalized Pareto distribution;

[0120] is the scale parameter The first prior distribution of

[0121] is the shape parameter The first prior distribution of

[0122] Furthermore, the first prior distribution includes normal distribution or gamma distribution.

[0123] In the S02:

[0124] In some embodiments, based on the debris flow disaster history data, the number of debris flow occurrences each year is counted to obtain a set , using the Poisson process to fit the over-scale M The frequency of debris flows λ , assuming that the number of debris flow events follows a Poisson process, Representative T The scale of debris flow in the year exceeds M The number of times is n The probability of is the intensity parameter of the Poisson distribution, that is, the frequency of debris flow, time Exceeding the scale The probability distribution density of debris flow events is:

[0125] ;

[0126] In the formula, the parameter λ is estimated using the Bayesian method, and the estimation methods include:

[0127] S021. For each parameter λ, select a second prior distribution;

[0128] S022. Construct a second likelihood function. ;

[0129] S023. Calculate the second posterior distribution. According to Bayes' theorem, is the second prior distribution of parameter λ, then the second posterior distribution is: ;

[0130] S024. Taking the mean or median of the second posterior distribution as the estimated value of parameter λ;

[0131] T For time;

[0132] is the selected debris flow scale;

[0133] is the number of debris flows occurring each year;

[0134] Expressed as time Exceeding the scale the number of debris flows;

[0135] is the number of debris flows;

[0136] is the intensity parameter of the Poisson process, and is also the frequency of debris flow;

[0137] is the strength parameter The second prior distribution of

[0138] is the strength parameter The second posterior distribution of .

[0139] Furthermore, the second prior distribution includes normal distribution or gamma distribution.

[0140] Furthermore, suppose that the first debris flow exceeds The time is ,make ,but The probability density distribution function and expectation of are:

[0141] ;

[0142] ;

[0143] Depend on Available, time Exceeding the scale The expectation of debris flow events is:

[0144] ;

[0145] because and Equivalently, the two are combined to get: ,Will Bring in The relationship between debris flow scale and frequency is obtained:

[0146] , where:

[0147] For time;

[0148] is the scale of the selected debris flow;

[0149] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0150] is the scale parameter of the generalized Pareto distribution;

[0151] is the shape parameter of the generalized Pareto distribution;

[0152] k The time when the first debris flow scale exceeds M occurs;

[0153] is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows.

[0154] Understandable, change T The value of M Value, when T = 100, the corresponding M is the scale of debris flow that occurs once in a hundred years.

[0155] In a second aspect, the embodiment of the present application provides a debris flow scale-frequency calculation system based on disaster history data, including:

[0156] A debris flow disaster history data acquisition module is used to obtain debris flow disaster history data;

[0157] The debris flow scale-frequency relationship acquisition module is used to obtain the debris flow scale-frequency relationship. The debris flow scale-frequency relationship is: , where:

[0158] For time;

[0159] is the scale of the selected debris flow;

[0160] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0161] is the scale parameter of the generalized Pareto distribution;

[0162] is the shape parameter of the generalized Pareto distribution;

[0163] is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows.

[0164] In the debris flow disaster history data acquisition module:

[0165] In some embodiments, the debris flow disaster history data includes the name of the debris flow ditch, the time when the debris flow ditch occurred, the location of the debris flow ditch, and the amount of debris flow.

[0166] It is understandable that the debris flow disaster history data is obtained based on literature or survey reports.

[0167] Furthermore, the generalized Pareto distribution is used to fit the distribution of debris flow discharge exceeding the threshold. Assuming that N debris flow events occur in a debris flow gully, the debris flow scales are recorded as , , ,···, , assuming that the scale of debris flow is a random event, the scale of debris flow exceeding scale M obeys the generalized Pareto distribution, and the probability density function of the generalized Pareto distribution is:

[0168] ;

[0169] The distribution function of the generalized Pareto distribution is

[0170] ;

[0171] In the formula, the parameters take Hill Determine by graph method, specifically: , , ,···, Arrange them in descending order into a new sequence , Hill The statistic is defined as , by point The curve formed is called Hill Figure, the data corresponding to the horizontal coordinate of the starting point of the stable area in the figure Threshold ;

[0172] parameter and Bayesian methods are used for estimation, including:

[0173] For parameters and , select the first prior distribution;

[0174] Construct the first likelihood function, ;

[0175] Calculate the first posterior distribution, according to Bayes' theorem, , The parameters are and The first prior distribution of , then the first posterior distribution is expressed as: ;

[0176] Take the first posterior distribution mean or median as a parameter and An estimated value of

[0177] M is the selected debris flow scale;

[0178] X It is a collection of debris flow gully scale;

[0179] For the i The scale of the debris flow gully;

[0180] , , ,···, are the scales of each debris flow gully;

[0181] are the scales of each debris flow gully, in descending order;

[0182] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0183] is the scale parameter of the generalized Pareto distribution;

[0184] is the shape parameter of the generalized Pareto distribution;

[0185] is the scale parameter The first prior distribution of

[0186] is the shape parameter The first prior distribution of

[0187] Furthermore, the first prior distribution includes normal distribution or gamma distribution.

[0188] In the module for obtaining the relationship between debris flow scale and frequency:

[0189] In some embodiments, based on the debris flow disaster history data, the number of debris flow occurrences each year is counted to obtain a set , using the Poisson process to fit the over-scale M The frequency of debris flows λ , assuming that the number of debris flow events follows a Poisson process, Representative T The scale of debris flow in the year exceeds M The number of times is n The probability of is the intensity parameter of the Poisson distribution, that is, the frequency of debris flow, time Exceeding the scale The probability distribution density of debris flow events is:

[0190] ;

[0191] In the formula, the parameter λ is estimated using the Bayesian method, and the estimation methods include:

[0192] For each parameter λ, a second prior distribution is selected;

[0193] Construct the second likelihood function, ;

[0194] Calculate the second posterior distribution. According to Bayes' theorem, is the second prior distribution of parameter λ, then the second posterior distribution is: ;

[0195] Take the mean or median of the second posterior distribution as the estimated value of parameter λ;

[0196] T For time;

[0197] is the selected debris flow scale;

[0198] is the number of debris flows occurring each year;

[0199] Expressed as time Exceeding the scale the number of debris flows;

[0200] is the number of debris flows;

[0201] is the intensity parameter of the Poisson process, and is also the frequency of debris flow;

[0202] is the strength parameter The second prior distribution of

[0203] is the strength parameter The second posterior distribution of .

[0204] Furthermore, the second prior distribution includes normal distribution or gamma distribution.

[0205] Furthermore, suppose that the first debris flow exceeds The time is ,make ,but The probability density distribution function and expectation of are:

[0206] ;

[0207] ;

[0208] Depend on Available, time Exceeding the scale The expectation of debris flow events is:

[0209] ;

[0210] because and Equivalently, the two are combined to get: ,Will Bring in The relationship between debris flow scale and frequency is obtained:

[0211] , where:

[0212] For time;

[0213] is the scale of the selected debris flow;

[0214] is the location parameter of the generalized Pareto distribution, or called the threshold;

[0215] is the scale parameter of the generalized Pareto distribution;

[0216] is the shape parameter of the generalized Pareto distribution;

[0217] k The time when the first debris flow scale exceeds M occurs;

[0218] is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows.

[0219] In a third aspect, the present application provides a computer device including a storage device and a processor, wherein the storage device stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for calculating the scale-frequency of debris flow based on disaster history data.

[0220] The computer device may be a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with the user through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0221] The memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D interface display memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory can also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is often used to store the operating system and various application software installed on the computer device, such as the program code of the debris flow scale-frequency calculation method based on disaster history data, etc. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0222] The processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the program code stored in the memory or process data, such as running the program code of the debris flow scale-frequency calculation method based on disaster history data.

[0223] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned method for calculating the scale-frequency of debris flow based on disaster history data.

[0224] The computer-readable storage medium stores an interface display program, and the interface display program can be executed by at least one processor so that the at least one processor executes the steps of the debris flow scale-frequency calculation method based on disaster history data as described above.

[0225] Experimental example

[0226] See also Figures 1 to 6 Taking the debris flow gully in Wenchuan area as an example, the debris flow scale-frequency calculation method based on disaster history data is used to calculate the debris flow scale-frequency.

[0227] The debris flow data of disaster history contains 80 debris flow occurrence records. The debris flow scale distribution is as follows: Figure 1 The debris flow year-frequency graph is shown in Figure 2 Threshold u Select 4.4, parameters Select normal distribution Normal (1,1000) as a priori, parameters Select truncated normal distribution TruncatedNormal (0, 1, -0.6, 0.6) as the prior, parameters λ Select normal distribution Normal (1,10) as a prior. Use MCMC The sampling was 10,000 times, the burning period was set to 2,000, and 4 chains were run in parallel. Figure 3 is the posterior distribution of parameter σ, Figure 4 is the posterior distribution of parameter ξ, Figure 5 For parameters λ The posterior distribution of Figure 6 is the debris flow scale-frequency relationship diagram, according to Figure 6 The red posterior mean curve shows that the scale of a mudslide in the region once in ten years is 910,000 cubic meters, the scale of a mudslide once in fifty years is 1.13 million cubic meters, and the scale of a mudslide once in a hundred years is 1.2 million cubic meters.

[0228] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server or network device, etc.) to execute the debris flow scale-frequency calculation method based on disaster history data described in the embodiment of the present application.

[0229] Figure 3 , Figure 4 and Figure 5 In it, mean refers to the average value in the posterior probability distribution, HDI (Highest Density Interval) refers to the highest density interval, which is a statistic that describes the numerical interval containing the highest probability density in the probability distribution, and 95% HDI refers to the interval containing 95% of the highest probability density in a given distribution.

[0230] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A debris flow scale-frequency calculation method based on disaster history data, characterized in that: include: Obtain historical data on debris flow disasters; The relationship between the scale and frequency of debris flow is obtained. , where: For time; is the scale of the selected debris flow; is the location parameter of the generalized Pareto distribution, or called the threshold; is the scale parameter of the generalized Pareto distribution; is the shape parameter of the generalized Pareto distribution; is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows; Among them, the method for obtaining the relationship between debris flow scale and frequency includes: According to the historical data of debris flow disasters, the number of debris flow occurrences each year is counted and the , using the Poisson process to fit the over-scale M The frequency of debris flows λ , assuming that the number of debris flow events follows a Poisson process, Representative T The scale of debris flow in the year exceeds M The number of times is n The probability of is the intensity parameter of the Poisson distribution, that is, the frequency of debris flow, time Exceeding the scale The probability distribution density of debris flow events is: ； In the formula, the parameter λ is estimated using the Bayesian method, and the estimation methods include: For each parameter λ, a second prior distribution is selected; Construct the second likelihood function, ; Calculate the second posterior distribution. According to Bayes' theorem, is the second prior distribution of parameter λ, then the second posterior distribution is: ; Take the mean or median of the second posterior distribution as the estimated value of parameter λ; T For time; is the selected debris flow scale; is the number of debris flows occurring each year; Expressed as time Exceeding the scale the number of debris flows; is the number of debris flows; is the intensity parameter of the Poisson process, and is also the frequency of debris flow; is the strength parameter The second prior distribution of is the strength parameter The second posterior distribution of Assume that the first debris flow exceeds the scale The time is ,make ,but The probability density distribution function and expectation of are: ； ； Depend on Available, time Exceeding the scale The expectation of debris flow events is: ； because and Equivalently, the two are combined to get: ,Will Bring in The relationship between debris flow scale and frequency is obtained: 。 2. The method for calculating the scale and frequency of debris flow based on disaster history data according to claim 1, characterized in that: The debris flow disaster history data include the name of the debris flow gully, the time of occurrence of the debris flow gully, the location of the debris flow gully and the amount of debris flow.

3. The method for calculating the scale and frequency of debris flow based on disaster history data according to claim 2, characterized in that: The generalized Pareto distribution is used to fit the distribution of debris flow discharge exceeding the threshold. Assuming that N debris flow events occur in a debris flow gully, the debris flow scales are recorded as , , ,···, , assuming that the scale of debris flow is a random event, the scale of debris flow exceeding scale M obeys the generalized Pareto distribution, and the probability density function of the generalized Pareto distribution is: ； The distribution function of the generalized Pareto distribution is ； In the formula, the parameters take Hill Determine by graph method, specifically: , , ,···, Arrange them in descending order into a new sequence , Hill The statistic is defined as , by point The curve formed is called Hill Figure, the data corresponding to the horizontal coordinate of the starting point of the stable area in the figure Threshold ; parameter and Bayesian methods are used for estimation, including: For parameters and , select the first prior distribution; Construct the first likelihood function, ; Calculate the first posterior distribution, according to Bayes' theorem, , The parameters are and The first prior distribution of , then the first posterior distribution is expressed as: ; Take the first posterior distribution mean or median as a parameter and An estimated value of M is the selected debris flow scale; X It is a collection of debris flow gully scale; For the i The scale of the debris flow gully; , , ,···, are the scales of each debris flow gully; are the scales of each debris flow gully, in descending order; is the location parameter of the generalized Pareto distribution, or called the threshold; is the scale parameter of the generalized Pareto distribution; is the shape parameter of the generalized Pareto distribution; is the scale parameter The first prior distribution of is the shape parameter The first prior distribution of .

4. The method for calculating the scale and frequency of debris flow based on disaster history data according to claim 3, characterized in that: The first prior distribution includes a normal distribution or a gamma distribution.

5. The method for calculating debris flow scale and frequency based on disaster history data according to claim 1, characterized in that: The second prior distribution includes a normal distribution or a gamma distribution.

6. A debris flow scale-frequency calculation system based on disaster history data, characterized in that: include: A debris flow disaster history data acquisition module is used to obtain debris flow disaster history data; The debris flow scale-frequency relationship acquisition module is used to obtain the debris flow scale-frequency relationship. The debris flow scale-frequency relationship is: , where: For time; is the scale of the selected debris flow; is the location parameter of the generalized Pareto distribution, or called the threshold; is the scale parameter of the generalized Pareto distribution; is the shape parameter of the generalized Pareto distribution; is the intensity parameter of the Poisson distribution, which is also above the threshold The frequency of debris flows; Among them, the method for obtaining the relationship between debris flow scale and frequency includes: According to the historical data of debris flow disasters, the number of debris flow occurrences each year is counted and the , using the Poisson process to fit the over-scale M The frequency of debris flows λ , assuming that the number of debris flow events follows a Poisson process, Representative T The scale of debris flow in the year exceeds M The number of times is n The probability of is the intensity parameter of the Poisson distribution, that is, the frequency of debris flow, time Exceeding the scale The probability distribution density of debris flow events is: ； In the formula, the parameter λ is estimated using the Bayesian method, and the estimation methods include: For each parameter λ, a second prior distribution is selected; Construct the second likelihood function, ; Calculate the second posterior distribution. According to Bayes' theorem, is the second prior distribution of parameter λ, then the second posterior distribution is: ; Take the mean or median of the second posterior distribution as the estimated value of parameter λ; T For time; is the selected debris flow scale; is the number of debris flows occurring each year; Expressed as time Exceeding the scale the number of debris flows; is the number of debris flows; is the intensity parameter of the Poisson process, and is also the frequency of debris flow; is the strength parameter The second prior distribution of is the strength parameter The second posterior distribution of Assume that the first debris flow exceeds the scale The time is ,make ,but The probability density distribution function and expectation of are: ； ； Depend on Available, time Exceeding the scale The expectation of debris flow events is: ； because and Equivalently, the two are combined to get: ,Will Bring in The relationship between debris flow scale and frequency is obtained: 。 7. Computer device, characterized in that The invention comprises a storage and a processor, wherein the storage stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 5.

Images