Composite flood disaster risk assessment method and system

Abstract

The present application provides a composite flood disaster risk assessment method and system, the method comprising the following steps: collecting disaster-related data in the study area; calculating the confluence time of the composite flood basin, constructing two composite flood sequences, and calculating the proportion of overlapping years between the two; optimizing the Copula function based on the Akaike Information Criterion; calculating the joint distribution probability of the two composite flood sequences according to the preferred Copula function, and sampling from the two composite flood sequences to construct a composite flood sampling sequence; inputting the composite flood sampling sequence into the constructed flood loss assessment model to obtain the flood loss value; calculating the composite flood disaster risk in the study area based on the expected annual loss according to the joint distribution probability of the composite flood sampling sequence and the calculated corresponding flood loss value. The present application realizes accurate assessment of composite flood disaster risk, and provides an important scientific basis for flood prevention and disaster reduction in the basin and the scientific implementation of flood warning.

Description

Technical Field

[0001] The present application relates to the technical field of flood disaster risk assessment, and in particular to a composite flood disaster risk assessment method and system. Background Art

[0002] The current definition of flood risk can be divided into three categories:

[0003] (1) Consequence loss category: Risk is considered to be a loss under certain probability conditions, and the flood risk is mainly quantified by multiplying the loss by the probability of flood occurrence;

[0004] (2) Possibility and probability type: Risk is considered to be the probability of occurrence of a disaster-causing factor, and frequency analysis is mainly used to assess risk;

[0005] (3) Conceptual formula type: from the perspective of disaster system theory, it is believed that risk is the result of the combined effects of hazard factors, exposure and vulnerability, and the indicator weight method is mainly used to assess flood risk. Flood risk assessment based on consequence losses can clearly reflect the consequences of flood disasters and provide quantifiable indicators for flood prevention and disaster reduction work, so it has been widely used in current research.

[0006] Expected annual loss is a commonly used indicator to quantify the size of flood risk. The basis for using expected annual loss to accurately assess flood risk is to be able to accurately calculate the probability of flood occurrence and the losses caused by it.

[0007] The magnitude of flood losses is affected by many factors, including the degree of flood danger, the degree of exposure of local population and property, the vulnerability of the disaster-bearing body itself, and early warning conditions. The complex and nonlinear relationship between flood losses and factors such as precipitation and runoff seriously affects the accuracy of flood loss calculation and flood risk assessment. Therefore, using machine learning methods that can handle complex and nonlinear relationships to reveal the response mechanism of flood losses to flood disasters can help improve the accuracy of flood risk assessment. On the other hand, unlike traditional univariate flood frequency analysis, the disaster-causing process of compound floods often involves the mutual influence and combined effect of multiple disaster-causing factors, and is not always caused by extreme precipitation or extreme flow. The mutual influence and combined effect of two non-extreme precipitation or flow events may also lead to compound floods. Therefore, it is necessary to further study the composition of compound floods and improve the accuracy of compound flood occurrence probability calculation by analyzing multiple compound flood sequences. Summary of the invention

[0008] The present application provides a composite flood disaster risk assessment method and system, which can solve the technical problem in the prior art of lacking an assessment method by further studying the composition of composite floods and obtaining a variety of composite flood sequences to improve the accuracy of composite flood occurrence probability calculation.

[0009] In a first aspect, the present application provides a composite flood disaster risk assessment method, comprising the following steps:

[0010] Collect disaster-related factor data and historical disaster data in the study area;

[0011] Calculate the confluence time of the composite flood basin, construct two composite flood sequences based on the confluence time: annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and calculate the proportion of overlapping years between the two.

[0012] Based on the Akaike information criterion, the Copula function for calculating the joint distribution probability of composite flood sequences is optimized, and the Copula function for joint distribution analysis of composite floods in different basins is suitable;

[0013] According to the preferred Copula function, the joint distribution probability of the two composite flood sequences is calculated, and samples are taken from the two composite flood sequences according to the proportion of overlapping years to construct a composite flood sampling sequence for assessing flood risks;

[0014] Construct a flood loss assessment model, input the composite flood sampling sequence into the constructed flood loss assessment model, and obtain the flood loss value;

[0015] According to the joint distribution probability of the composite flood sampling sequence and the calculated corresponding flood loss value, the composite flood disaster risk of the study area is obtained based on the expected annual loss calculation.

[0016] In combination with the first aspect, in one implementation, constructing two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation according to the confluence time specifically includes the following steps:

[0017] Identify the annual maximum precipitation sequence and annual maximum runoff sequence from the precipitation sequence and runoff sequence in historical disaster data;

[0018] Calculate the detection range of the corresponding maximum precipitation and the corresponding maximum runoff according to the confluence time;

[0019] Identify the maximum values ​​of precipitation sequences and runoff sequences within the detection range, and construct composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation.

[0020] In combination with the first aspect, in one implementation, the calculation of the overlapping year ratio TP of the two is as shown in the following formula:

[0021]

[0022] Where Yearsame is the number of years of the overlapping parts of the composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and Yeartotal is the length of the composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation in the study area.

[0023] In combination with the first aspect, in one implementation, the Copula function for calculating the probability of joint distribution of composite flood sequences based on the Akaike information criterion is preferably applicable to the Copula function for joint distribution analysis of composite floods in different river basins, and specifically includes the following steps:

[0024] The Akaike Information Criterion is used to select the optimal marginal distribution of single variables in two composite floods.

[0025] The Akaike Information Criterion is used to determine the optimal bivariate Copula function for two types of composite floods.

[0026] According to the optimal marginal distribution and the optimal bivariate Copula function, the Copula function for calculating the joint distribution probability of the composite flood sequence is selected, which is suitable for the Copula function for the joint distribution analysis of composite floods in different basins.

[0027] In combination with the first aspect, in one embodiment, in the Copula function step of calculating the joint distribution probability of the composite flood sequence based on the Akaike Information Criterion, the selection range of the optimal marginal distribution of the single variable includes P-III distribution, LN3 distribution, Gumbel distribution and GEV distribution; the selection range of the optimal bivariate Copula function includes Independence Copula function, Gaussian Copula function, Student t Copula function (t-Copula), Gumbel Copula function, Clayton Copula function, Frank Copula function, Joe Copula function, BB1 Copula function, BB8 Copula function, Tawn type 1 Copula function and Tawn type 2 Copula function.

[0028] In combination with the first aspect, in one embodiment, the method of calculating the joint distribution probability of the two composite flood sequences according to the preferred Copula function, sampling from the two composite flood sequences according to the proportion of overlapping years, and constructing a composite flood sampling sequence for assessing flood risks specifically includes the following steps:

[0029] According to the proportion of overlapping years, samples are taken from the annual maximum precipitation and annual maximum runoff frequency distributions to obtain the annual maximum precipitation-annual maximum runoff composite flood sequence;

[0030] Input the annual maximum precipitation-annual maximum runoff composite flood sequence into the Copula function preferably suitable for joint distribution analysis of composite floods in different basins, obtain the joint distribution probability of two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and obtain the average of the joint distribution probabilities of the two composite flood sequences as the joint distribution probability of the composite flood sequence;

[0031] According to the proportion of overlapping years, a group of composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation with greater losses are obtained from the annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation composite flood Copula sampling;

[0032] A group of annual maximum precipitation-corresponding maximum runoff or annual maximum runoff-corresponding maximum precipitation composite flood series with greater losses and the obtained annual maximum precipitation-annual maximum runoff composite flood series are merged to obtain a composite flood sampling series for flood risk assessment.

[0033] In combination with the first aspect, in one implementation, constructing a flood loss assessment model, inputting a composite flood sampling sequence into the constructed flood loss assessment model, and obtaining a flood loss value specifically includes the following steps:

[0034] Select the factors with strong correlation from the collected factors related to flood disaster as the input of support vector machine;

[0035] The flood loss assessment model was constructed by using support vector machines combined with the selected highly correlated factors and historical disaster data;

[0036] Input the composite flood sampling sequence into the constructed flood loss assessment model to obtain the flood loss value.

[0037] In combination with the first aspect, in one implementation, the composite flood disaster risk of the study area is obtained based on the composite flood sampling sequence and its joint distribution probability and the calculated corresponding flood loss value, based on the expected annual loss calculation, as shown in the following formula:

[0038]

[0039] Where D(p,r) is the flood loss value under the condition of given precipitation value p and given runoff value r, and f(p,r) is the joint distribution probability under the condition of given precipitation value p and given runoff value r.

[0040] In a second aspect, the present application provides a composite flood disaster risk assessment system, comprising:

[0041] The assessment data collection module is used to collect disaster-related factor data and historical disaster data in the study area;

[0042] A composite flood sequence and overlapped year ratio acquisition module is connected to the evaluation data collection module for calculating the confluence time of the composite flood basin, constructing two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation according to the confluence time, and calculating the overlapped year ratio of the two;

[0043] The joint distribution probability calculation function optimization module is used to optimize the Copula function for the joint distribution probability calculation of the composite flood sequence based on the Akaike information criterion, which is suitable for the Copula function for the joint distribution analysis of composite floods in different basins;

[0044] The joint distribution probability and composite sampling sequence acquisition module is used to calculate the joint distribution probability of two composite flood sequences according to the preferred Copula function, and to sample from the two composite flood sequences according to the proportion of overlapping years to construct a composite flood sampling sequence for assessing flood risks;

[0045] A flood loss value acquisition module is connected to the joint distribution probability and composite sampling sequence acquisition module, and is used to construct a flood loss assessment model, input the composite flood sampling sequence into the constructed flood loss assessment model, and obtain the flood loss value;

[0046] The disaster risk assessment module is communicatively connected with the joint distribution probability and composite sampling sequence acquisition module and the flood loss value acquisition module, and is used to obtain the composite flood disaster risk of the study area based on the expected annual loss calculation according to the composite flood sampling sequence and its joint distribution probability and the calculated corresponding flood loss value.

[0047] In conjunction with the second aspect, in one implementation, the joint distribution probability and composite sampling sequence acquisition module includes:

[0048] The maximum precipitation and maximum runoff value acquisition unit is in communication connection with the composite flood sequence and the coincidence year proportion acquisition module, and is used to sample from the annual maximum precipitation and annual maximum runoff frequency distributions according to the coincidence year proportion, respectively, to obtain the annual maximum precipitation-annual maximum runoff composite flood sequence;

[0049] A joint distribution probability acquisition unit is communicatively connected with the maximum precipitation and maximum runoff value acquisition unit, and is used for inputting the annual maximum precipitation-annual maximum runoff composite flood sequence into a Copula function preferably suitable for joint distribution analysis of composite floods in different river basins, obtaining the joint distribution probability of two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and obtaining the average value of the joint distribution probability of the two composite flood sequences as the joint distribution probability of the composite flood sequence;

[0050] A composite flood sequence screening unit is in communication connection with the joint distribution probability acquisition unit, and is used to obtain a group of composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation with greater losses from the annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation composite flood Copula sampling according to the proportion of overlapping years;

[0051] A composite flood sampling sequence acquisition unit is communicatively connected to the composite flood sampling sequence acquisition unit, and is used to merge a group of annual maximum precipitation-corresponding maximum runoff or annual maximum runoff-corresponding maximum precipitation composite flood sequences with greater losses and the acquired annual maximum precipitation-annual maximum runoff composite flood sequence to obtain a composite flood sampling sequence for flood risk assessment.

[0052] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0053] The present application provides a composite flood disaster risk assessment method, which identifies multiple composite flood sequences through confluence time, calculates the probability of joint distribution of composite floods in the basin in combination with the Copula function, samples from their joint distribution in proportion to the proportion of overlapping years of multiple composite flood sequences, accurately reflects the composition of composite floods, and assesses the risk of composite flood disasters in combination with the flood loss assessment model and the annual expected loss. This is of great significance for further understanding the occurrence mechanism of composite floods, and also provides an important scientific basis for flood prevention and disaster reduction in the basin and the scientific implementation of flood warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a process flow of a composite flood disaster risk assessment method provided in an embodiment of the present application;

[0055] Figure 2 A schematic diagram of the construction of two composite flood sequences in a certain river basin and their overlapping parts provided in an embodiment of the present application;

[0056] Figure 3 This is a rendering of the flood loss assessment model constructed by the support vector machine provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0059] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0060] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0061] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0062] First, some technical terms in the present application are explained to facilitate those skilled in the art to understand the present application.

[0063] Compound floods are floods caused by multiple hazards.

[0064] Composite flood series are data series related to different parameters of floods caused by multiple disaster factors;

[0065] The composite flood sampling sequence is a data sequence for model analysis of floods caused by multiple disaster factors;

[0066] EAD, expected annual loss;

[0067] AIC, Akaike information criterion Akaike information criterion.

[0068] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0069] First, please refer to Figure 1 The present application embodiment provides a composite flood disaster risk assessment method, comprising the following steps:

[0070] Step S1, collecting disaster-related factor data and historical disaster data in the study area;

[0071] Step S2, calculate and obtain the confluence time of the composite flood basin, construct two composite flood sequences of annual maximum precipitation-corresponding maximum runoff (AMP-CPD) and annual maximum runoff-corresponding maximum precipitation (APD-CMP) according to the confluence time, and calculate the proportion of overlapping years between the two;

[0072] Step S3, based on the Akaike Information Criterion, the Copula function for calculating the joint distribution probability of the composite flood sequence is selected, and the Copula function suitable for the joint distribution analysis of composite floods in different basins is selected;

[0073] Step S4, calculating and obtaining the joint distribution probability of the two composite flood sequences according to the preferred Copula function, and sampling from the two composite flood sequences to construct a composite flood sampling sequence for assessing flood risk;

[0074] Step S5: construct a flood loss assessment model, input the composite flood sampling sequence into the constructed flood loss assessment model, and obtain the flood loss value;

[0075] Step S6: According to the joint distribution probability of the composite flood sampling sequence and the calculated corresponding flood loss value, the composite flood disaster risk of the study area is calculated based on the expected annual loss.

[0076] The present application provides a composite flood disaster risk assessment method, which identifies multiple composite flood sequences through confluence time, calculates the probability of joint distribution of composite floods in the basin in combination with the Copula function, samples from their joint distribution in proportion to the proportion of overlapping years of multiple composite flood sequences, accurately reflects the composition of composite floods, and assesses the risk of composite flood disasters in combination with the flood loss assessment model and the annual expected loss. This is of great significance for further understanding the occurrence mechanism of composite floods, and also provides an important scientific basis for flood prevention and disaster reduction in the basin and the scientific implementation of flood warnings.

[0077] In one embodiment, the disaster-related factor data in step S1 includes hydrological and meteorological data, water system data and socio-economic data, more specifically:

[0078] The hydrological and meteorological data include daily runoff and precipitation data. The daily runoff data of the hydrological stations are derived from the Hydrological Yearbook of the People's Republic of China.

[0079] Daily precipitation data of meteorological stations are from the National Meteorological Data Center (http: / / data.cma.cn);

[0080] The water system data include water system networks at all levels and sub-basin division data in the study area, which were obtained from the Resources and Environmental Science and Data Center of the Chinese Academy of Sciences (https: / / www.resdc.cn / );

[0081] Socioeconomic data, including population, gross national product, and greening rate, were obtained from the Resources and Environmental Science and Data Center of the Chinese Academy of Sciences (https: / / www.resdc.cn / ), the China City Statistical Yearbook, and the China Urban Construction Statistical Yearbook.

[0082] In one embodiment, the historical disaster data in step S1 includes data such as direct economic losses, casualties or affected areas of crops caused by historical flood disasters in the study area, which are obtained from the "China Flood and Drought Disaster Bulletin" of the Ministry of Water Resources and the yearbooks and statistical yearbooks of the provinces in the basin.

[0083] In one embodiment, the confluence time of the composite flood basin is calculated and obtained in step S2, as shown in the following formula:

[0084] (1)

[0085] In the formula, is the confluence time, which refers to the time it takes for precipitation to fall on the basin and converge at the basin outlet. A is the basin area in km 2 .

[0086] In one embodiment, the present application obtains multiple composite flood sequences of a composite flood basin by identifying the confluence time. In step S2, two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation are constructed according to the confluence time, specifically including the following steps:

[0087] The Maximum function in the built-in package of R language is used to identify the annual maximum precipitation sequence and annual maximum runoff sequence from the precipitation sequence and runoff sequence of historical disaster data. The annual maximum precipitation sequence and annual maximum runoff sequence are the maximum daily precipitation sequence and the maximum daily runoff sequence of the year. Generally, the precipitation data obtained is on a daily scale, that is, one precipitation data value per day. Here, the maximum daily precipitation sequence of the year refers to the precipitation on the day with the most precipitation in a year, and the maximum daily runoff sequence of the year refers to the runoff value on the day with the largest runoff in a year.

[0088] Calculate the corresponding according to the confluence time (correspondence refers to the time correspondence within the same basin. For example, the annual maximum daily precipitation occurs on July 15, and the confluence time is 5 days, then the corresponding maximum runoff is the maximum runoff between July 15 and July 20. Correspondingly, if the annual maximum daily runoff occurs on July 15, then the corresponding maximum daily precipitation is the maximum precipitation between July 10 and July 15.

[0089] The detection range of maximum precipitation and corresponding maximum runoff is shown in the following formula:

[0090] (2)

[0091] In the formula, range is the detection range, LT is the confluence time, is the date of the maximum daily precipitation in the year, t r It is the date when the maximum daily runoff occurs in the year;

[0092] Identify the maximum values ​​of precipitation sequence and runoff sequence within the detection range, and construct the composite flood sequence of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, such as Figure 2 As shown, the corresponding maximum runoff and the corresponding maximum precipitation are respectively the maximum daily runoff within the LT time range from the occurrence day of the annual maximum daily precipitation to the subsequent LT time range and the maximum daily precipitation within the LT time range from the occurrence day of the annual maximum daily runoff to the previous LT time range.

[0093] In this application, the corresponding in the two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation refers to corresponding events in time within the same basin. The corresponding maximum runoff in the annual maximum precipitation-corresponding maximum runoff sequence is the maximum runoff corresponding to the time of annual maximum precipitation, and the corresponding maximum precipitation in the annual maximum runoff-corresponding maximum precipitation sequence is the maximum precipitation corresponding to the time of annual maximum runoff.

[0094] In one embodiment, in step S2, the proportion of overlapping years is calculated, which refers to the ratio of the number of identical years in the annual maximum precipitation-corresponding maximum runoff and the annual maximum runoff-corresponding maximum precipitation composite flood sequences to the total length of the sequences, and the calculation formula is as follows:

[0095] (3)

[0096] In the formula, is the proportion of overlapping years, Year same The number of years when the composite flood sequence of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation overlaps. total It is the length of the composite flood sequence of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation in the study area.

[0097] In one embodiment, the calculation results of the percentage of overlapping years between the two in the study area are shown in Table 1.

[0098] Table 1 The percentage of overlapping years of the overlapping parts of the annual maximum precipitation-corresponding maximum runoff and the annual maximum runoff-corresponding maximum precipitation sequences

[0099]

[0100] In one embodiment, the step S3, based on the Akaike information criterion, preferably the Copula function for calculating the joint distribution probability of the composite flood sequence, is suitable for the Copula function for the joint distribution analysis of composite floods in different basins, and specifically includes the following steps:

[0101] Step S31, using AIC to select the optimal marginal distribution of the single variable in the two composite floods, the selection range of the optimal marginal distribution of the single variable includes P-III distribution, LN3 distribution, Gumbel distribution and GEV distribution;

[0102] Step S32, using AIC to determine the optimal two-variable Copula functions for the composite flood, wherein the selection range of the optimal two-variable Copula function includes Independence Copula function, Gaussian Copula function, Student tCopula function (t-Copula), Gumbel Copula function, Clayton Copula function, Frank Copula function, Joe Copula function, BB1 Copula function, BB8 Copula function, Tawn type 1 Copula function and Tawntype 2 Copula function;

[0103] The above-mentioned optimal marginal distribution selection of a single variable and the determination of the optimal bivariate Copula function are both based on the minimum AIC, where the calculation formula of AIC is:

[0104] (4)

[0105] In the formula, is the maximum likelihood estimate of the parameter β, and p is the number of parameters.

[0106] Step S33, based on the optimal marginal distribution and the optimal bivariate Copula function, the Copula function for calculating the probability of joint distribution of composite flood sequences is preferably selected, and the Copula function for joint distribution analysis of composite floods in different river basins is preferably selected, and is specifically implemented as follows:

[0107] Step S331, according to the two constructed composite flood sequences, the P-III type distribution calls the ppearsonIII function in the R language PearsonDS package, the Gumbel distribution calls the pgumbel function in the R language ismev package, LN3 writes the corresponding R language code implementation, and the GEV distribution calls the pgev function in the R language VGAM package. The above four distributions are applied to fit the single variables in the two composite flood sequences, and the maximum likelihood method is used for parameter estimation. The AIC is used to determine the optimal marginal distribution of the single variable, and the AIC calculation calls the AIC function in the R language EnvCpt package. The optimal marginal distribution of the single variable in the two composite flood sequences in the study area and the optimal distribution results of the annual maximum precipitation and annual maximum runoff sequences are shown in Table 2.

[0108] Table 2 Optimal distribution of annual maximum precipitation and annual maximum runoff series and optimal marginal distribution of single variables in two composite flood series

[0109]

[0110] Step S332: Based on step S331, the Bicopselect function in the VineCopula package of the R language is used to construct the joint distribution function of the two composite flood sequences, and the AIC is used to determine the optimal Copula function.

[0111] In a specific embodiment, the calculation results of the optimal Copula function for two composite flood sequences in the study area are shown in Table 3.

[0112] Table 3 Optimal Copula functions for two composite flood sequences

[0113]

[0114] In one embodiment, the step S4, according to the preferred Copula function, calculates and obtains the joint distribution probability of the two composite flood sequences, and samples from the two composite flood sequences according to the proportion of overlapping years to construct a composite flood sampling sequence for assessing flood risks, specifically includes the following steps:

[0115] Step S41, according to the optimal distribution of the annual maximum precipitation and annual maximum runoff sequences in Table 2, the rpearsonIII function in the PearsonDS package of the R language, the rgumbel function in the ismev package, the runif function in the built-in program package, and the rgev function in the VGAM package are used accordingly. According to the proportion of overlapping years, samples are taken from the annual maximum precipitation and annual maximum runoff frequency distributions to obtain the annual maximum precipitation-annual maximum runoff composite flood sequence. If 1000 samples are required, of which the proportion of overlapping years is 20%, it means that 200 samples need to be taken from the optimal distribution of the annual maximum precipitation and annual maximum runoff sequences respectively;

[0116] Step S42, input the annual maximum precipitation-annual maximum runoff composite flood sequence into the Copula function preferably suitable for joint distribution analysis of composite floods in different watersheds, apply the pMvdc function in the R language VineCopula package to obtain the joint distribution probabilities of the two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and obtain the average value of the joint distribution probabilities of the two composite flood sequences as the joint distribution probability of the composite flood sequence;

[0117] Step S43, according to the proportion of overlapping years, use the rMvdc function in the R language VineCopula package to sample from the joint distribution function of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation "with the same joint distribution probability". The joint distribution function is constructed using the Copula method. If 1000 samples are required and the proportion of overlapping years is 20%, step S41 has sampled 200 times. This step needs to sample 800 times from the joint distribution function of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation respectively, to obtain two sequences, each of which has a length of 800;

[0118] Step S44, merge a group of annual maximum precipitation-corresponding maximum runoff or annual maximum runoff-corresponding maximum precipitation composite flood sequences with greater losses in step S43 and the annual maximum precipitation-annual maximum runoff composite flood sequence in step S41, and obtain a composite flood sampling sequence for flood risk assessment; specifically, by inputting the two groups of values ​​in step S43 into the flood loss assessment model constructed in step S5, the losses of the two groups of values ​​are obtained respectively, and the group with greater losses is selected. A group of values ​​here refers to a pair of sampling values ​​of length 800 mentioned above, which contains two values. For the sampling value of annual maximum precipitation-corresponding maximum runoff, one value is the annual maximum precipitation and the other value is the corresponding maximum runoff. For the sampling value of annual maximum runoff-corresponding maximum precipitation, one value is the annual maximum runoff and the other value is the corresponding maximum precipitation. Merge with the annual maximum precipitation-annual maximum runoff composite flood sequence of length 200 in step S42 to obtain the required composite flood sampling sequence of length 1000;

[0119] Through step S4, sampling is performed proportionally from the joint distribution of multiple composite flood sequences according to the proportion of overlapping years in the multiple composite flood sequences, so as to accurately reflect the composition of the composite floods.

[0120] In this application, frequency distribution refers to the frequency distribution function of a single variable, such as the frequency distribution function of annual maximum precipitation or annual maximum runoff. Joint distribution is a function, taking annual maximum precipitation-corresponding maximum runoff as an example, which is the joint distribution function of annual maximum precipitation and corresponding maximum runoff, while joint distribution probability is a probability value, which is the probability of a certain annual maximum precipitation value and a certain corresponding maximum runoff value occurring at the same time.

[0121] In one embodiment, the step S5, constructing a flood loss assessment model, inputting a composite flood sampling sequence into the constructed flood loss assessment model, and obtaining a flood loss value, specifically includes the following steps:

[0122] Step S51, using the cor function in the R language package to select factors with strong correlation from the collected factors related to the flood disaster according to the Pearson correlation coefficient, and use them as inputs of the support vector machine;

[0123] Step S52: Use the svm function in the e1071 package of the R language to implement the application of support vector machine to combine the screened out highly correlated factors and historical disaster data to reveal the relationship between flood losses and precipitation and flow, build a flood loss assessment model, and use the determination coefficient (R 2 ) to measure the model effect. The flood loss assessment model constructed by support vector machine is shown in Figure 3 As shown, the determination coefficient of the rate period is 0.80, and the determination coefficient of the validation period is 0.77, which indicates that the constructed flood loss assessment model can effectively simulate and predict flood losses;

[0124] Step S53: input the composite flood sampling sequence into the constructed flood loss assessment model to obtain the flood loss value.

[0125] In one embodiment, in step S6, the composite flood disaster risk of the study area is obtained based on the expected annual loss calculation according to the composite flood sampling sequence and its joint distribution probability and the calculated corresponding flood loss value. The expected annual loss (EAD) calculation formula is as follows:

[0126] (5)

[0127] Where EAD is the expected annual loss calculation value, D(p,r) is the flood loss value under the condition of given precipitation value p and given runoff value r, f(p,r) is the joint distribution probability under the condition of given precipitation value p and given runoff value r, and given precipitation value p and given runoff value r are the precipitation value and runoff value in the composite flood sampling sequence.

[0128] In a specific embodiment, the calculation results of the expected annual loss in the study area are shown in Table 4.

[0129] Table 4 Calculation results of expected loss in 2014

[0130]

[0131] The above composite flood disaster risk assessment results show that the flood risk in the source area of ​​the basin (Catchment 4) and the downstream area of ​​the basin (Catchment 3) is relatively small, with expected annual losses of 632 million yuan and 755 million yuan respectively. The flood risk in the middle reaches of the basin (Catchment 1) and the areas with large lakes in the basin (Catchment 7, Catchment 8 and Catchment 9) is relatively large, with expected annual losses of 5.043 billion yuan, 8.956 billion yuan, 6.678 billion yuan and 4.232 billion yuan respectively. The upstream areas of the basin and the basin tributaries face medium-sized flood risks, with expected annual losses of 2.344-3.577 billion yuan.

[0132] The above description is only an example implementation of this application and is not intended to limit this application. The selection of frequency distribution function, Copula function and flood loss assessment model in this application can be set according to needs and specific research areas. Any modification, equivalent replacement, improvement, etc. made within the scope of the claims of this application shall be within the scope of protection of this application.

[0133] On the second aspect, the present application provides a composite flood disaster risk assessment system, including an assessment data collection module, a composite flood sequence and overlapping year proportion acquisition module, a joint distribution probability calculation function optimization module, a joint distribution probability and composite sampling sequence acquisition module, a flood loss value acquisition module and a disaster risk assessment module.

[0134] Among them, the evaluation data collection module is used to collect disaster-related factor data and historical disaster data in the study area; the composite flood sequence and overlap year ratio acquisition module is communicated with the evaluation data collection module, and is used to calculate the confluence time of the composite flood basin, and construct two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation according to the confluence time, and calculate the overlap year ratio of the two; the joint distribution probability calculation function optimization module is used to optimize the Copula function of the composite flood sequence joint distribution probability calculation based on the Akaike information criterion, which is suitable for the Copula function of the joint distribution analysis of composite floods in different basins; the joint distribution probability and composite sampling sequence acquisition module is used to optimize the Copula function of the composite flood sequence joint distribution probability calculation based on the preferred Copula function. ula function, calculates and obtains the joint distribution probability of two composite flood sequences, and samples from the two composite flood sequences according to the proportion of overlapping years to construct a composite flood sampling sequence for assessing flood risks; a flood loss value acquisition module is communicated with the joint distribution probability and composite sampling sequence acquisition module to construct a flood loss assessment model, inputs the composite flood sampling sequence into the constructed flood loss assessment model, and obtains the flood loss value; a disaster risk assessment module is communicated with the joint distribution probability and composite sampling sequence acquisition module and the flood loss value acquisition module to obtain the composite flood disaster risk of the study area based on the expected annual loss calculation according to the joint distribution probability of the composite flood sampling sequence and the calculated corresponding flood loss value.

[0135] In one embodiment, the joint distribution probability and composite sampling sequence acquisition module includes a maximum precipitation and maximum runoff value acquisition unit, a joint distribution probability acquisition unit, a composite flood sequence screening unit and a composite flood sampling sequence acquisition unit.

[0136] Among them, the maximum precipitation and maximum runoff value acquisition unit is communicated with the composite flood sequence and the overlapped year ratio acquisition module, and is used to sample from the annual maximum precipitation and annual maximum runoff frequency distributions according to the overlapped year ratio, and obtain the annual maximum precipitation-annual maximum runoff composite flood sequence; the joint distribution probability acquisition unit is communicated with the maximum precipitation and maximum runoff value acquisition unit, and is used to input the annual maximum precipitation-annual maximum runoff composite flood sequence into a Copula function preferably suitable for joint distribution analysis of composite floods in different river basins, and obtain the joint distribution probability of the two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and obtain the average of the joint distribution probabilities of the two composite flood sequences. value, as the joint distribution probability of the composite flood sequence; a composite flood sequence screening unit, which is in communication connection with the joint distribution probability acquisition unit, and is used to obtain a group of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation composite flood sequences with greater losses from the annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation composite flood Copula sampling according to the proportion of overlapping years; a composite flood sampling sequence acquisition unit, which is in communication connection with the composite flood sampling sequence acquisition unit, and is used to merge a group of annual maximum precipitation-corresponding maximum runoff or annual maximum runoff-corresponding maximum precipitation composite flood sequences with greater losses and the acquired annual maximum precipitation-annual maximum runoff composite flood sequence to obtain a composite flood sampling sequence for flood risk assessment.

[0137] The functional implementation of each module in the above-mentioned composite flood disaster risk assessment system corresponds to each step in the above-mentioned composite flood disaster risk assessment method embodiment, and its functions and implementation processes are not described one by one here.

[0138] In a third aspect, an embodiment of the present application provides a composite flood disaster risk assessment device, which may be a device with data processing capabilities, such as a personal computer (PC), a laptop computer, or a server.

[0139] In the embodiment of the present application, the composite flood disaster risk assessment device may include a processor, a memory, a communication interface, and a communication bus.

[0140] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0141] The communication interface includes input / output (I / O) interface, physical interface and logical interface, etc., which are used to realize the interconnection of devices inside the composite flood disaster risk assessment device, and the interface used to realize the interconnection between the composite flood disaster risk assessment device and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0142] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0143] The processor may be a general-purpose processor, which may call the composite flood disaster risk assessment program stored in the memory and execute the composite flood disaster risk assessment method provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the composite flood disaster risk assessment program is called may refer to the various embodiments of the composite flood disaster risk assessment method of the present application, which will not be described in detail here.

[0144] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0145] The readable storage medium of the present application stores a composite flood disaster risk assessment program, wherein when the composite flood disaster risk assessment program is executed by a processor, the steps of the composite flood disaster risk assessment method as described above are implemented.

[0146] Among them, the method implemented when the composite flood disaster risk assessment program is executed can refer to the various embodiments of the composite flood disaster risk assessment method of the present application, and will not be repeated here.

[0147] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0148] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially 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) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0149] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A composite flood disaster risk assessment method, characterized in that: The following steps are involved: Collect disaster-related factor data and historical disaster data in the study area; Calculate the confluence time of the composite flood basin, construct two composite flood sequences based on the confluence time: annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and calculate the proportion of overlapping years between the two. The method of constructing two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation according to the confluence time specifically includes the following steps: Identify the annual maximum precipitation sequence and annual maximum runoff sequence from the precipitation sequence and runoff sequence in historical disaster data; Calculate the detection range of the corresponding maximum precipitation and the corresponding maximum runoff according to the confluence time; Identify the maximum values ​​of precipitation sequence and runoff sequence within the detection range, and construct the composite flood sequence of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation; Based on the Akaike information criterion, the Copula function for calculating the joint distribution probability of the composite flood sequence is selected; According to the selected Copula function, the joint distribution probability of the two composite flood sequences is calculated, and samples are taken from the two composite flood sequences according to the proportion of overlapping years to construct a composite flood sampling sequence for assessing flood risks, which specifically includes the following steps: According to the proportion of overlapping years, samples are taken from the annual maximum precipitation and annual maximum runoff frequency distributions to obtain the annual maximum precipitation-annual maximum runoff composite flood sequence; Input the annual maximum precipitation-annual maximum runoff composite flood sequence into the selected Copula function suitable for joint distribution analysis of composite floods in different basins, obtain the joint distribution probability of the two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and obtain the average of the joint distribution probabilities of the two composite flood sequences as the joint distribution probability of the composite flood sequence; According to the proportion of overlapping years, a group of composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation with greater losses are obtained from the annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation composite flood Copula sampling; A group of composite flood sequences of annual maximum precipitation-corresponding maximum runoff or annual maximum runoff-corresponding maximum precipitation with greater losses and the obtained composite flood sequence of annual maximum precipitation-annual maximum runoff are merged to obtain a composite flood sampling sequence for flood risk assessment; Construct a flood loss assessment model, input the composite flood sampling sequence into the constructed flood loss assessment model, and obtain the flood loss value; The method of constructing a flood loss assessment model, inputting a composite flood sampling sequence into the constructed flood loss assessment model, and obtaining a flood loss value specifically includes the following steps: Select the factors with strong correlation from the collected factors related to flood disaster as the input of support vector machine; The flood loss assessment model was constructed by using support vector machines combined with the selected highly correlated factors and historical disaster data; Input the composite flood sampling sequence into the constructed flood loss assessment model to obtain the flood loss value; According to the joint distribution probability of the composite flood sampling sequence and the calculated corresponding flood loss value, the composite flood disaster risk of the study area is obtained based on the expected annual loss calculation.

2. The composite flood disaster risk assessment method according to claim 1, characterized in that: The calculation of the proportion of overlapping years between the two is as follows: In the formula, Year same The number of years when the composite flood sequence of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation overlaps. total It is the length of the composite flood sequence of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation in the study area.

3. The composite flood disaster risk assessment method according to claim 1, characterized in that: The Copula function for calculating the probability of joint distribution of composite flood sequences is selected based on the Akaike information criterion, and is applicable to the Copula function for joint distribution analysis of composite floods in different river basins, and specifically includes the following steps: The Akaike Information Criterion is used to select the optimal marginal distribution of single variables in two composite floods; The Akaike Information Criterion is used to determine the optimal bivariate Copula function for two types of composite floods. According to the optimal marginal distribution and the optimal bivariate Copula function, the Copula function for calculating the joint distribution probability of composite flood sequences is selected, which is suitable for the Copula function for joint distribution analysis of composite floods in different basins.

4. The composite flood disaster risk assessment method according to claim 3, characterized in that: In the step of selecting the Copula function for calculating the joint distribution probability of the composite flood sequence based on the Akaike Information Criterion, the selection range of the optimal marginal distribution of the single variable includes P-III distribution, LN3 distribution, Gumbel distribution and GEV distribution; the selection range of the optimal bivariate Copula function includes Independence Copula function, Gaussian Copula function, Student t Copula function (t-Copula), Gumbel Copula function, Clayton Copula function, Frank Copula function, JoeCopula function, BB1 Copula function, BB8 Copula function, Tawn type 1 Copula function and Tawn type 2Copula function.

5. The composite flood disaster risk assessment method according to claim 1, characterized in that: According to the joint distribution probability of the composite flood sampling sequence and the calculated corresponding flood loss value, the composite flood disaster risk of the study area is calculated based on the expected annual loss, as shown in the following formula: Where EAD is the expected annual loss calculation value, D(p,r) is the flood loss value under the condition of given precipitation value p and given runoff value r, f(p,r) is the joint distribution probability under the condition of given precipitation value p and given runoff value r, and given precipitation value p and given runoff value r are the precipitation value and runoff value in the composite flood sampling sequence.

6. A system using the composite flood disaster risk assessment method according to any one of claims 1 to 5, characterized in that: include: The assessment data collection module is used to collect disaster-related factor data and historical disaster data in the study area; A composite flood sequence and overlapped year ratio acquisition module is connected to the evaluation data collection module for calculating the confluence time of the composite flood basin, constructing two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation according to the confluence time, and calculating the overlapped year ratio of the two; The joint distribution probability calculation function selection module is used to select the Copula function for the joint distribution probability calculation of the composite flood sequence based on the Akaike information criterion, which is suitable for the Copula function for the joint distribution analysis of composite floods in different basins; The joint distribution probability and composite sampling sequence acquisition module is used to calculate the joint distribution probability of two composite flood sequences according to the selected Copula function, and to sample from the two composite flood sequences according to the proportion of overlapping years to construct a composite flood sampling sequence for assessing flood risks; A flood loss value acquisition module is connected to the joint distribution probability and composite sampling sequence acquisition module, and is used to construct a flood loss assessment model, input the composite flood sampling sequence into the constructed flood loss assessment model, and obtain the flood loss value; The disaster risk assessment module is communicatively connected with the joint distribution probability and composite sampling sequence acquisition module and the flood loss value acquisition module, and is used to obtain the composite flood disaster risk of the study area based on the expected annual loss calculation according to the joint distribution probability of the composite flood sampling sequence and the calculated corresponding flood loss value.

7. The system according to claim 6, characterized in that The joint distribution probability and composite sampling sequence acquisition module includes: The maximum precipitation and maximum runoff value acquisition unit is in communication connection with the composite flood sequence and the coincidence year proportion acquisition module, and is used to sample from the annual maximum precipitation and annual maximum runoff frequency distributions according to the coincidence year proportion, respectively, to obtain the annual maximum precipitation-annual maximum runoff composite flood sequence; A joint distribution probability acquisition unit is communicatively connected with the maximum precipitation and maximum runoff value acquisition unit, and is used to input the annual maximum precipitation-annual maximum runoff composite flood sequence into the selected Copula function, obtain the joint distribution probability of two composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation, and obtain the average value of the joint distribution probability of the two composite flood sequences as the joint distribution probability of the composite flood sequence; A composite flood sequence screening unit is in communication connection with the joint distribution probability acquisition unit, and is used to obtain a group of composite flood sequences of annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation with greater losses from the annual maximum precipitation-corresponding maximum runoff and annual maximum runoff-corresponding maximum precipitation composite flood Copula sampling according to the proportion of overlapping years; A composite flood sampling sequence acquisition unit is communicatively connected with the composite flood sampling sequence acquisition unit, and combines a group of annual maximum precipitation-corresponding maximum runoff or annual maximum runoff-corresponding maximum precipitation composite flood sequences with greater losses, and the annual maximum precipitation-annual maximum runoff composite flood sequence, to obtain a composite flood sampling sequence for flood risk assessment.

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