Mountain torrent susceptibility evaluation method coupling hydrological model and logistic regression

Abstract

The invention discloses a mountain torrent susceptibility evaluation method coupling a hydrological model and logistic regression. The method comprises the following steps: constructing the hydrological model; calculating the mountain torrent disaster forming flow of each first-level sub-basin, setting different rainfall scenes, and simulating the flood process of each first-level sub-basin under different rainfall scenes by using a hydrological model; selecting a rainfall session which can best reflect a basin mountain torrent disaster formation general rule, and collecting mountain torrent disaster formation flow data of each primary sub-basin for the rainfall session for constructing a mountain torrent susceptibility evaluation model; extracting features of each primary sub-basin as evaluation factors, performing mountain torrent susceptibility evaluation factor preliminary screening based on a Pearson correlation coefficient method, and completing screening of mountain torrent susceptibility evaluation parameters based on LASSO regression on the basis of a preliminary screening result; and evaluating the mountain torrent susceptibility of the sub-basins based on logistic regression. Compared with the prior art, the method can conveniently and efficiently judge the mountain torrent susceptibility of the drainage basin.

Description

Technical Field

[0001] The invention relates to the field of flash flood susceptibility assessment and risk management, and in particular to a flash flood susceptibility assessment method coupling a HEC-HMS model with logistic regression. Background Art

[0002] Flash flood susceptibility is a description of the difficulty of flash flood disasters in a certain area or basin. It refers to the possibility of flood events that threaten human life and property safety, production and living environment. Rainfall is the main external factor affecting flash flood disasters. The impact of rainfall on flash floods reflects the conditional probability of flash flood disasters under the influence of inducing factors; the internal factors for the occurrence and development of flash flood disasters are mainly geological environmental conditions, including topography, water network structure, hydrogeology and other factors, which reflect the potential possibility of flash flood disasters in a certain area.

[0003] According to the principle of flood formation, when rainfall falls on the underlying surface, the runoff formed in the same basin will converge through the slope and river channel and gather at the outlet of the basin. The basin is the place where flash floods breed and erupt, and it is also the basic unit of flash flood disaster risk management, which can reflect the evolution of natural characteristics. Therefore, clarifying the relationship between the basin topography and flash flood susceptibility is the only way to improve the ability to prevent and control flash flood disasters. Summary of the invention

[0004] In order to solve the problem of flash flood susceptibility evaluation, the present invention aims to propose a flash flood susceptibility evaluation method that couples hydrological model and logistic regression, takes sub-basin as evaluation unit, and basin topography and geomorphic parameters as evaluation factors, and establishes a flash flood susceptibility evaluation model by coupling hydrological model and logistic regression method.

[0005] The present invention provides a flash flood susceptibility assessment method by coupling a hydrological model with logistic regression, comprising:

[0006] S1, conduct hydrological analysis on the watershed and divide it into sub-basins, and build a hydrological model based on the sub-basin division results;

[0007] S2, select the flash flood warning section of the first-level sub-basin, use the flat water level of the flash flood warning section as the flash flood warning water level, collect the flash flood warning water levels, and calculate the flash flood disaster flow of each first-level sub-basin;

[0008] S3, setting different rainfall scenarios, and using the hydrological model constructed in step S1 to simulate the flood process of each first-level sub-basin under different rainfall scenarios;

[0009] S4, obtain the peak flow according to the flood process simulation results, analyze the flash flood disaster situation of each sub-basin under different rainfall scenarios, select the rainfall scenario with the peak flow exceeding the flash flood warning flow of each first-level sub-basin as the rainfall event that best reflects the general law of flash flood disaster in the basin, and build a flash flood susceptibility evaluation model for this rainfall event;

[0010] S5, extract the characteristics of each first-level sub-basin as the evaluation factor, conduct the preliminary screening of flash flood susceptibility evaluation factors based on the Pearson correlation coefficient method, and complete the screening of flash flood susceptibility evaluation parameters based on the LASSO regression on the basis of the preliminary screening results;

[0011] S6, constructing a flash flood susceptibility assessment model based on logistic regression, and conducting a flash flood susceptibility assessment of the sub-basin according to calculation results of the flash flood susceptibility assessment model.

[0012] In some embodiments, S1 further includes calibrating and validating parameters of a semi-distributed hydrological model based on relative error of peak flow, relative error of runoff depth, peak-to-appearance time difference, and certainty coefficient.

[0013] In some implementations, the expression of the flash flood flow Q of the first-level sub-basin is as follows:

[0014]

[0015] Among them, Q is the flash flood disaster flow in the first-level sub-basin, n is the roughness, A is the cross-sectional area corresponding to the flash flood disaster water level, R is the hydraulic radius, and J is the river gradient.

[0016] In some embodiments, the first-level sub-watershed characteristics in S5 include at least watershed scale, topography, shape and water network structure.

[0017] In some embodiments, S5 further includes: performing preliminary screening of flash flood susceptibility evaluation factors based on the Pearson correlation coefficient method, selecting two indicator parameters with a correlation coefficient between watershed characteristic parameters greater than or equal to 0.8, calculating the sum of the absolute values ​​of the correlation coefficients of the two indicators with all other indicators except each other, and then deleting the parameters with large corresponding result values.

[0018] In some embodiments, the correlation between two variables X and Y is defined as follows:

[0019]

[0020] Among them, ρ is the correlation coefficient, cov(X,Y) is the covariance between the pairwise calculation results X and Y of all characteristic parameters in the flash flood susceptibility assessment model, E(X) and E(Y) are the overall means of the variables, and σ X is the standard deviation of X, σ Yis the standard deviation of Y, X i and Y i is the value of the i-th individual in the sample, and I represents the number of individuals in the sample.

[0021] In some embodiments, the screening of flash flood susceptibility evaluation parameters based on LASSO regression in S5 further includes: selecting relatively independent evaluation factors that have an impact on flash flood evolution for flash flood susceptibility evaluation model construction, and explaining the estimated value of the regression coefficient of the variable The expression is as follows:

[0022]

[0023] Among them, X is the explanatory variable matrix composed of the factors affecting the groundwater level, Y is the target variable for groundwater level prediction, β j is the regression coefficient of the jth groundwater level influencing factor, λ is the penalty factor, p is the total number of explanatory variables, The minimum function value of the calculated parameter.

[0024] The flash flood susceptibility evaluation model in S6 is expressed as follows:

[0025] g(x)=b0+b1x1+b2x2+…+b n x n

[0026]

[0027] In landslide hazard assessment, g(x) is the occurrence of flash floods under the influence of flash flood disaster factors, represented by 0 or 1, 0 means flash floods do not occur, 1 means flash floods occur, x1, x2,…, x n is the influencing factor of flash flood susceptibility, b0 is the constant term of the flash flood susceptibility assessment model, b1, b2, … b n is the logistic regression coefficient corresponding to the influencing factor of flash flood susceptibility.

[0028] In some implementations, when multiple parameters affecting flash floods are combined and calculated to obtain g(x), an evaluation of changes in the likelihood of flash flood disasters is performed.

[0029] In some implementations, the probability value of flash flood disaster occurrence is between 0 and 1, which is used to divide the flash flood susceptibility level into five levels: very low, low, medium, high, and very high.

[0030] Compared with the prior art, the beneficial technical effects and technical progress achieved by the present invention are as follows:

[0031] 1) Using the hydrological model to simulate the flood process of each first-level sub-basin under different rainfall scenarios, obtain the flood peak flow, analyze the flash flood disaster situation of each sub-basin under the different rainfall scenarios, select the rainfall event that best reflects the general law of flash flood disasters in the basin, and construct a flash flood susceptibility evaluation model under this rainfall event; on this basis, the flash flood susceptibility evaluation factors are initially screened based on the Pearson correlation coefficient method, and the flash flood susceptibility evaluation parameters based on LASSO regression are screened. The hydrological model and the flash flood susceptibility evaluation parameters based on LASSO regression are screened. The two interact and influence each other and even combine to judge the flash flood susceptibility of the basin based on the geomorphological characteristics and the development characteristics of the river system, which is convenient and efficient;

[0032] 2) In the process of completing the screening of flash flood susceptibility evaluation parameters based on LASSO regression, the logistic regression method is further combined to construct a flash flood susceptibility evaluation model. By comprehensively evaluating the flash flood susceptibility level of the basin, the ability to prevent, control and control flash floods is improved, providing a quantitative basis for the construction of a flash flood defense system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is an overall flow chart of a flash flood susceptibility assessment method of coupling hydrological model and logistic regression of the present invention;

[0034] Figure 2 It is an algorithm block diagram of an embodiment of a flash flood susceptibility evaluation method of coupling a hydrological model and logistic regression according to the present invention;

[0035] Figure 3 This is an example diagram of the division of sub-basins in the study basin;

[0036] Figure 4 This is an example diagram of the rate-periodic flood process of the HEC-HMS hydrological model constructed in the study basin;

[0037] Figure 5 This is an example diagram of the flood process during the validation period of the HEC-HMS hydrological model constructed in the study basin;

[0038] Figure 6 It is to study the location distribution map of early warning sections in each sub-basin of the basin;

[0039] Figure 7 This is an example diagram of the flood process of each first-level sub-basin simulated by the HEC-HMS hydrological model under different design rainfall scenarios;

[0040] Figure 8 This is an example of the calculation results of the correlation coefficient of the watershed characteristic parameters;

[0041] Fig. 9 It is a graded distribution map of the susceptibility of flash floods in the study basin. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is described below in conjunction with specific embodiments and drawings.

[0043] like Figure 1 As shown, the specific steps of the flash flood susceptibility evaluation method of coupling hydrological model and logistic regression of the present invention are as follows:

[0044] Step 1: Conduct hydrological analysis on the target basin based on the basin digital elevation model (DEM) and divide it into sub-basins. Combined with the sub-basin division results, a hydrological model is constructed, specifically the HEC-HMS model, to calibrate and verify the model parameters. Specifically, it includes:

[0045] The HEC-HMS model is as follows:

[0046] According to the rainfall-runoff characteristics of the study basin, the SCS curve method, SCS unit line method, exponential decay method and Muskingum method were selected to calculate the runoff generation, confluence, base flow and river channel evolution.

[0047] (1) SCS curve method

[0048]

[0049] In the above formula, R t is the accumulated net rainfall at time t, mm; P is the rainfall depth at time t, mm; I a is the initial rainfall loss, mm; S is the maximum soil water storage capacity, which is a measure of the ability to absorb and intercept rainfall in the sub-basin, mm.

[0050] (2) SCS unit line method

[0051]

[0052] In the above formula, Q p is the peak flow rate per unit line, T p is the time of peak flow rate per unit line, C is the conversion constant, which is 2.08 in the international system of units; A is the basin area, km 2 .

[0053] (3) Exponential decay method

[0054] The exponential decay model assumes that the relationship between the base flow at a certain moment and the initial base flow is as follows:

[0055] Q bt =Q b0 D t

[0056] In the above formula, Q bt is the base flow at time t, m 3 / s;Qb0 is the base flow at the initial moment, m 3 / s; D is the attenuation index, which is the ratio of the base flow at time t to the base flow of the previous day, and its value range is 0 to 1.

[0057] (4) Muskingum Method

[0058] Q2=C1I2+C2I1+C3Q1

[0059]

[0060] C1+C2+C3=1

[0061] In the above formula, Δt is the time step, h; I1 and I2 are the starting and ending flow rates of the upstream section, m 3 / s; Q1 and Q2 are the starting and ending flow rates of the downstream section, m 3 / s; K is the slope of the channel storage curve, which is the time it takes for the flood wave to pass through the river section, h; x is the flow density factor, a dimensionless number.

[0062] Step 1-1: Based on the basin DEM, perform hydrological analysis on the target basin and divide it into sub-basins, build a semi-distributed hydrological model HEC-HMS, and use the model to simulate the formation process of flash floods. Figure 3 As shown in Figure 1, the first-level sub-basin was selected as the key research object to obtain the sub-basin division results of the research basin. The rainfall runoff data of the study area from 1981 to 2019 were collected, and 10 representative flood processes with complete data and induced flash flood disasters were selected. Six flash floods were selected to complete the calibration of model parameters, and four flash floods were used to verify the forecasting effect of the model. The specific flood processes used for calibration and verification are as follows: Figure 4 and Figure 5 As shown;

[0063] Step 1-2, based on the relative error of flood peak flow, relative error of runoff depth, peak time difference and certainty coefficient as the model accuracy evaluation indicators, the accuracy of the HEC-HMS model constructed in step 1-1 is evaluated to achieve the calibration and verification of hydrological model parameters;

[0064] The calculation formulas for each indicator are as follows:

[0065] Peak flow relative error RE Q :

[0066]

[0067] Among them, Q O is the measured peak flow, Q S is the peak flow calculated by HEC-HMS model, and the relative error of peak flow RE Q The allowed margin of error is 20%;

[0068] Runoff depth relative error RE R :

[0069]

[0070] Among them, R O is the measured runoff depth, R S The runoff depth is calculated by the HEC-HMS model simulation. When the runoff depth is greater than 20 mm, the allowable error is 20 mm; when it is less than 3 mm, the allowable error is 3 mm.

[0071] Peak time difference ΔT:

[0072] ΔT=T S -T O (3)

[0073] Among them, T O is the measured flood peak time, T S is the peak time obtained by model simulation; ΔT is 30% of the time interval between the predicted time and the measured peak time as the allowable error. When the allowable error is less than 3h (hours) or the length of a period calculated by the model, 3h or a calculation period is used as the allowable error.

[0074] Determinism coefficient DC:

[0075]

[0076] Among them, n is the length of the data time series, i is the time series data index, Q c (i) is the flow rate of the i-th sample calculated by the model, Q o (i) is the measured flow rate of the i-th sample, is the mean of the measured flow rate.

[0077] As shown in Table 1, the calibration and verification results of the HEC-HMS model (basin flood simulation system) are shown. The model has a good effect on flash flood simulation, among which the certainty coefficients of flood events in the calibration period are all above 0.7, with an average value of 0.803; the certainty coefficients in the verification period are all above 0.8, with an average value of 0.889. The flood peak error and runoff depth error of the calibration period and verification period floods are within 20%, and the peak time difference is less than 2h, which has a high simulation accuracy for the flood process.

[0078] Table 1

[0079]

[0080] Step 2: Select the flash flood warning section of the first-level sub-basin and collect the flash flood warning water level, and use the Manning formula to calculate the flash flood disaster flow in the sub-basin; specifically include

[0081] The first-level sub-basin is used as the flash flood susceptibility evaluation unit, and the flash flood disaster flow in each first-level sub-basin is calculated; the warning section is generally selected as the section with the lowest elevation within 1km of the basin outlet. If a section is not the lowest elevation, but the residential areas along the section are clustered, it can also be considered to be set as a warning section. The location of the warning section in each sub-basin is as follows: Figure 6 shown.

[0082] The flat water level of the warning section is used as the flash flood warning water level, and the flash flood disaster flow of each first-level sub-basin is calculated using the Manning formula. The Manning formula is as follows:

[0083]

[0084] in, Q is the flash flood disaster flow in the first-level sub-basin, n is the roughness, A is the cross-sectional area corresponding to the flash flood disaster water level, R is the hydraulic radius, and J is the river gradient.

[0085] According to the calculation results of Manning's formula, the disaster-causing flow in each sub-basin is shown in Table 2.

[0086] Table 2

[0087]

[0088] Step 3: Set different rainfall scenarios and use the hydrological model to simulate the flood process of each first-level sub-basin under different rainfall scenarios. The specific description is as follows:

[0089] According to the historical rainfall pattern of short rainfall duration and concentrated rainfall in the basin, the rainfall duration is set to 1h, 2h, and 3h, and the cumulative rainfall is set to 30mm, 40mm, and 50mm. The HEC-HMS model is used to simulate the flood process of each first-level sub-basin, and the flood process simulation results are obtained, such as Figure 7 As shown;

[0090] Step 4: Analyze the flood peak flow according to the flood process simulation results, and use whether the flood peak flow exceeds the flash flood warning flow of each first-level sub-basin as the judgment standard to analyze the flash flood disaster situation of each sub-basin under different rainfall scenarios, select the rainfall event that best reflects the general law of flash flood disasters in the basin, and collect the flash flood disaster flow data of each first-level sub-basin for this rainfall event for the construction of the flash flood susceptibility evaluation model; the specific description is as follows:

[0091] Whether the peak flow in the flood simulation results exceeds the flash flood warning flow in the basin is used as the judgment standard, and the flash flood disaster situation of each sub-basin under different rainfall scenarios is analyzed. The rainfall event that best reflects the general law of flash flood disasters in the basin is selected, that is, the flash flood disaster flow data of the rainfall event with a rainfall duration of 1h and a cumulative rainfall of 40mm. Among the 22 first-level sub-basins of the target, there are 12 disaster-causing sub-basins and 10 non-disaster-causing sub-basins. The above intermediate disaster samples are used to build the flash flood susceptibility evaluation model in step 6. The process of step 5 is the parameter screening of the model, and step 6 is the construction of the model;

[0092] Step 5: Taking the first-level sub-basin as the evaluation unit, extract the characteristics of each first-level sub-basin as the evaluation factor, and conduct a preliminary screening of the flash flood susceptibility evaluation factors based on the Pearson correlation coefficient method. Based on the preliminary screening results, the flash flood susceptibility evaluation parameters are screened based on the LASSO regression. The process of this step is the parameter screening operation of the flash flood susceptibility evaluation model. The specific parameters are shown in Table 3.

[0093] Table 3

[0094]

[0095]

[0096]

[0097] Step 5-1, based on the Pearson correlation coefficient method between the characteristic parameters of the watershed, the flash flood susceptibility evaluation factors are initially screened. By filtering the parameters with high correlation, the multicollinearity problem is preliminarily solved. The Pearson correlation coefficient is used to measure the correlation between two variables X and Y, and is defined as shown in formula (6):

[0098]

[0099] In the above formula, ρ is the correlation coefficient, cov(X,Y) is the covariance between the pairwise calculation results X and Y of all characteristic parameters in the flash flood susceptibility assessment model, E(X) and E(Y) are the overall means of any two basin characteristic parameter variables X and Y, respectively, and σ X is the standard deviation of X, σ Y is the standard deviation of Y, X i and Y i is the value of the i-th individual in the sample, and I represents the number of individuals in the sample; the pairwise calculation results X and Y between all characteristic parameters in the flash flood susceptibility assessment model are as follows: Figure 8 shown.

[0100] After completing the calculation of the correlation coefficient between parameters, select two indicator parameters with a correlation coefficient greater than or equal to 0.8, calculate the sum of the absolute values ​​of the correlation coefficients of the two indicators with all other indicators except each other, and then delete the parameters with larger corresponding result values ​​to complete the preliminary screening of parameters; based on the preliminary screening principle of parameters and the results of correlation coefficient calculation, the parameters involved in the construction of flash flood susceptibility are preliminarily screened, and 15 parameters are obtained as independent variables to input into the Lasso regression model, namely: average elevation of the basin, undulation, Melton ratio, average slope of the basin, slope ratio, area elevation integral, basin morphological factor, roundness rate, river length, river frequency, geomorphic structure ratio, branch ratio, river channel maintenance constant, river fineness ratio, and main channel length;

[0101] Step 5-2: Select the flash flood susceptibility evaluation factors based on LASSO regression. Select relatively independent evaluation factors that have an impact on flash flood evolution for flash flood susceptibility evaluation model construction. Explanatory variable regression coefficient estimation The expression is as follows:

[0102]

[0103] Among them, X is the explanatory variable matrix composed of the factors affecting the groundwater level, Y is the target variable for groundwater level prediction, β j is the regression coefficient of the jth groundwater level influencing factor, λ is the penalty factor, p is the total number of explanatory variables, is the minimum function value of the calculated parameter;

[0104] The 15 characteristic parameters obtained through the initial correlation screening are used as independent variables, 40mm -1 The disaster data of each sub-basin under the h rainfall scenario is taken as the dependent variable, and the model finally retains two evaluation factors, namely, the geomorphic structure ratio and the basin morphology factor;

[0105] Step 6: Based on logistic regression, a flash flood susceptibility evaluation model is constructed. According to the model calculation results, the flash flood susceptibility of the first-level sub-basin is divided into five levels, which are described as follows:

[0106] Step 6-1, the logistic regression model is a commonly used multivariate regression statistical method in the evaluation of natural disaster susceptibility. It is a typical regression analysis method for binary dependent variables. Logistic regression is based on the theory of linear regression. By introducing the Logistic function, the predicted value is constrained within the range of 0 to 1, so that logistic regression can handle binary classification problems. Then when the independent variable in the logistic regression is significant, the relationship between the occurrence of flash floods and the parameters is expressed as follows:

[0107] g(x)=b0+b1x1+b2x2+…+b n x n (8)

[0108]

[0109] In landslide hazard assessment, g(x) represents the occurrence of flash floods under the influence of flash flood disaster factors, represented by 0 or 1, 0 means flash floods do not occur, 1 means flash floods occur, x1, x2,…, x n is the influencing factor of flash flood susceptibility, b0 is the constant term of the flash flood susceptibility assessment model, b1, b2, … b n is the logistic regression coefficient corresponding to the flash flood susceptibility influencing factor. The probability of flash flood disasters occurring is between 0 and 1. The probability of flash flood disasters not occurring is obtained, and the expression is as follows:

[0110]

[0111] The ratio of the probability of a flash flood disaster to the probability of no flash flood disaster is odds, which is expressed as follows:

[0112] odds = probability of flash flood disaster occurring / probability of flash flood disaster not occurring = e g(x) (11)

[0113] When using the probability of flash flood disasters not occurring to evaluate flash flood disasters, in order to facilitate the analysis of the degree of change in the probability of flash flood disasters occurring, the logarithms of both sides are taken:

[0114] ln(odds)=g(x)=b0+b1x1+b2x2+…+b n x n (12)

[0115] After taking the logarithms of both sides, it is found that the degree of change in the probability of flash flood disasters is g(x). That is to say, when multiple parameters affecting flash floods are combined and calculated to obtain g(x), the change in the possibility of flash flood disasters can be reflected.

[0116] The parameters of the Dali River sub-basin geomorphic structure ratio and basin morphological factors were compared with the -1 Logistic regression analysis was performed on the corresponding sub-basin disaster data under the h rainfall scenario, and the parameter estimation results of the flash flood susceptibility assessment model were obtained as follows:

[0117] Table 4

[0118]

[0119] The flash flood susceptibility assessment model is constructed as follows:

[0120] f(x)=-11.824+1.937x1+2.115x2 (25)

[0121] In the above formula, x1 is the morphological factor of the flash flood susceptibility assessment factor, and x2 is the landform structure ratio of the flash flood susceptibility assessment factor.

[0122] Step 6-2, the probability P value of flash flood disasters is between 0 and 1, and the flash flood susceptibility level is divided into five levels: very low, low, medium, high and very high with a step size of 0.2; according to the flash flood susceptibility level classification standard, the flash flood susceptibility results of each sub-basin of the basin are studied, such as Fig. 9 shown.

[0123] The above implementation steps are only used to help understand the specific methods and core ideas of the present invention, and are not intended to limit the present invention. For researchers in this field who make several equivalent substitutions, combinations and modifications based on the ideas of the present invention without departing from the principles of the present invention, these improvements and modifications should also be considered to fall within the scope of protection of the present invention.

Claims

1. A flash flood susceptibility assessment method that couples a hydrological model with logistic regression, characterized in that: include: S1, conduct hydrological analysis on the watershed and divide it into sub-basins, and build a hydrological model based on the sub-basin division results; S2, select the flash flood warning section of the first-level sub-basin, use the flat water level of the flash flood warning section as the flash flood warning water level, collect the flash flood warning water levels, and calculate the flash flood disaster flow of each first-level sub-basin; S3, setting different rainfall scenarios, and using the semi-distributed hydrological model constructed in step S1 to simulate the flood process of each first-level sub-basin under different rainfall scenarios; S4, obtain the peak flow according to the flood process simulation results, analyze the flash flood disaster situation of each sub-basin under different rainfall scenarios, select the rainfall scenario with the peak flow exceeding the flash flood warning flow of each first-level sub-basin as the rainfall event that best reflects the general law of flash flood disaster in the basin, and build a flash flood susceptibility evaluation model for this rainfall event; S5, extract the characteristics of each first-level sub-basin as the evaluation factor, conduct the preliminary screening of flash flood susceptibility evaluation factors based on the Pearson correlation coefficient method, and complete the screening of flash flood susceptibility evaluation parameters based on the LASSO regression on the basis of the preliminary screening results; S6, constructing a flash flood susceptibility assessment model based on logistic regression, and using the calculation results of the flash flood susceptibility assessment model to evaluate the flash flood susceptibility of the sub-basin.

2. A flash flood susceptibility assessment method based on coupling hydrological model and logistic regression according to claim 1, characterized in that: S1 further includes calibration and verification of semi-distributed hydrological model parameters based on relative error of peak flow, relative error of runoff depth, peak-to-appearance time difference and deterministic coefficient.

3. The flash flood susceptibility assessment method of coupling hydrological model and logistic regression according to claim 1 is characterized in that: The expression of the flash flood flow Q in the first-level sub-basin is as follows: Among them, Q is the flash flood disaster flow in the first-level sub-basin, n is the roughness, A is the cross-sectional area corresponding to the flash flood disaster water level, R is the hydraulic radius, and J is the river gradient.

4. The flash flood susceptibility assessment method of coupling a semi-distributed hydrological model with logistic regression according to claim 1 is characterized in that: The characteristics of each first-level sub-basin in S5 at least include basin scale, topography, shape and water network structure.

5. The flash flood susceptibility assessment method of coupling hydrological model and logistic regression according to claim 1 is characterized in that: S5 further includes: conducting a preliminary screening of flash flood susceptibility evaluation factors based on the Pearson correlation coefficient method, selecting two indicator parameters with a correlation coefficient greater than or equal to 0.8 between the basin characteristic parameters, calculating the sum of the absolute values ​​of the correlation coefficients of the two indicators with all other indicators except each other, and then deleting the parameters with large corresponding result values.

6. A flash flood susceptibility assessment method coupling hydrological model and logistic regression according to claim 5, characterized in that: in, Correlation between two variables X and Y Correlation is defined as follows: Among them, ρ is the correlation coefficient, cov(X,Y) is the covariance between the pairwise calculation results X and Y of all characteristic parameters in the flash flood susceptibility assessment model, E(X) and E(Y) are the overall means of the variables, and σ X is the standard deviation of X, σ Y is the standard deviation of Y, X i and Y i is the value of the i-th individual in the sample, and I is the number of individuals in the sample.

7. The flash flood susceptibility assessment method of coupling hydrological model and logistic regression according to claim 1 is characterized in that: The screening of flash flood susceptibility evaluation parameters based on LASSO regression described in S5 further includes: selecting relatively independent evaluation factors that have an impact on flash flood evolution for flash flood susceptibility evaluation model construction, and estimating the regression coefficient of the explanatory variable The expression is as follows: Among them, X is the explanatory variable matrix composed of the factors affecting the groundwater level, Y is the target variable for groundwater level prediction, β j is the regression coefficient of the jth groundwater level influencing factor, λ is the penalty factor, p is the total number of explanatory variables, The minimum function value of the calculated parameter.

8. The flash flood susceptibility assessment method of coupling hydrological model and logistic regression according to claim 1 is characterized in that: The flash flood susceptibility evaluation model in S6 is expressed as follows: g(x)=b0+b1x1+b2x2+…+b n x n In landslide hazard assessment, g(x) is the occurrence of flash floods under the influence of flash flood disaster factors, represented by 0 or 1, 0 means flash floods do not occur, 1 means flash floods occur, x1, x2,…, x n is the influencing factor of flash flood susceptibility, b0 is the constant term of the flash flood susceptibility assessment model, b1, b2, … b n is the logistic regression coefficient corresponding to the influencing factor of flash flood susceptibility.

9. The flash flood susceptibility assessment method of coupling hydrological model and logistic regression according to claim 1 is characterized in that: When multiple parameters affecting flash floods are combined and calculated to obtain g(x), the change in the possibility of flash flood disasters is evaluated.

10. The flash flood susceptibility assessment method of coupling hydrological model and logistic regression according to claim 1 is characterized in that: The probability value of flash flood disasters is between 0 and 1, which is used to divide the flash flood susceptibility level into five levels: very low, low, medium, high and very high.

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