Typhoon and seawater backflow induced collapse damming prediction and early warning method

Abstract

The application discloses a typhoon and seawater backflow comprehensive induced collapse blocking river dam forming prediction and early warning method, and belongs to the technical field of disaster prediction and early warning. The method comprises the following specific steps: obtaining the spatial information and attribute information of the river slope into the sea, constructing a collapse potential degree calculation dataset and determining a calculation model, and calculating the collapse potential degree of each slope; calculating the typhoon and seawater backflow comprehensive induced collapse occurrence probability through a typhoon rainfall influence coefficient, a seawater backflow inundation enhancement coefficient and a crack present situation enhancement coefficient, and then calculating the typhoon and seawater backflow comprehensive induced collapse blocking river dam forming occurrence probability of each slope; and performing graded early warning according to the typhoon and seawater backflow comprehensive induced collapse blocking river dam forming occurrence probability. The application considers the particularity of the typhoon and seawater backflow comprehensive induced collapse, selects the optimal collapse potential degree calculation model, and comprehensively considers various factors of a disaster environment, so that the accuracy of the typhoon and seawater backflow comprehensive induced collapse blocking river dam forming prediction and early warning is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of disaster prediction and early warning, and particularly relates to a typhoon and seawater backflow comprehensive induced collapse damming prediction and early warning method. BACKGROUND

[0002] Typhoon is a kind of intense tropical cyclone, usually accompanied by strong wind, heavy rain, storm surge and other phenomena, which may cause serious damage to the disaster area. The damage caused by typhoon disaster chain is much greater than the typhoon disaster itself. Typhoon causes seawater backflow in the river into the sea, and under the comprehensive action of rainfall infiltration and existing cracks, the groundwater level of the river into the sea rises, the mechanical strength and deformation properties of the rock-soil body decrease, and the probability of geological disasters such as collapse increases. The collapse piles up into a dam after rushing into the river, blocking the river, and may cause downstream flood disasters after breaching. The existing collapse prediction and early warning methods have the following problems:

[0003] (1) The disaster environment induced by typhoon and seawater backflow is different from ordinary collapse, the existing method does not consider this particularity when selecting the potential degree hazard factor, and the existing method has subjectivity and randomness in model selection, resulting in large error in the calculation result of potential degree;

[0004] (2) The existing method only considers the influence of typhoon rainfall infiltration on the mechanical strength and deformation properties of the slope rock-soil body when analyzing the induction mechanism of typhoon on collapse, however, seawater backflow caused by typhoon rainfall may submerge the collapse body and reduce the slope stability, and the existing method ignores the comprehensive action of typhoon rainfall and seawater backflow;

[0005] (3) Typhoon often leads to continuous rainfall, even continuous rain in the north, the existing method generally uses process rainfall or landing day rainfall to represent the typhoon rainfall grade, which exaggerates the role of typhoon rainfall and ignores the influence of underlying surface factors, reducing the success rate of disaster chain prediction and the working efficiency of comprehensive prevention and control measures;

[0006] (4) Since the development process of typhoon and seawater backflow induced collapse is continuous and long, the initial damage form is the initiation and expansion of backwall cracks and side wall cracks, but the existing method focuses on potential degree and typhoon rainfall, ignoring the evolution process of existing cracks and the influence of complex disaster environment leading to crack development on the occurrence of collapse;

[0007] (5) The collapse body migration process is affected by the type and intensity of inducing factors, rock-soil body properties, topography, surface roughness, migration distance and other factors, and the volume of collapse body that may rush into the river has strong uncertainty, the existing method regards it as a fixed value, which does not conform to the complex mutual feedback mechanism of collapse damming and disaster environment. SUMMARY

[0008] To solve the above technical problems, the purpose of the present application is to provide a typhoon and seawater backflow comprehensive induced collapse blocking river dam forming prediction and early warning method, the specific steps are as follows:

[0009] To achieve the above purpose, the present application provides a typhoon and seawater backflow comprehensive induced collapse blocking river dam forming prediction and early warning method, the specific steps are as follows:

[0010] Step S1: Obtain the space information and attribute information of the river into the sea slope, construct the collapse potential degree calculation data set, determine the collapse potential degree hazard factor and scale;

[0011] Step S2: Determine the collapse potential degree calculation model, and calculate the collapse potential degree of each slope;

[0012] Step S3: Calculate the typhoon rainfall influence coefficient, seawater backflow inundation enhancement coefficient and crack status enhancement coefficient of each slope collapse;

[0013] Step S4: Calculate the typhoon and seawater backflow comprehensive induced collapse occurrence probability, and the calculation formula is as follows:

[0014] ;

[0015] Wherein: is the typhoon and seawater backflow comprehensive induced collapse occurrence probability, is the slope collapse potential degree, is the typhoon rainfall influence coefficient, is the seawater backflow inundation enhancement coefficient, is the crack status enhancement coefficient;

[0016] Step S5: Assume that the volume of the collapse body that can rush into the river channel is subject to sin θ · k 4· V Laplace distribution with mean and variance of 2, calculate the typhoon and seawater backflow comprehensive induced collapse blocking river dam forming probability of each slope:

[0017] ;

[0018] Wherein: is the typhoon and seawater backflow comprehensive induced collapse blocking river dam forming probability, is the river bed section area at the slope, is the dam length, is the collapse body transport loss index, is the potential collapse body volume, is the angle between the collapse body transport direction and the river;

[0019] Step S6: According to the typhoon and seawater backflow comprehensive induced collapse blocking river dam forming probability, the classification warning is carried out.

[0020] Preferably, step S1 is specifically as follows:

[0021] Step S11: Obtain the number of historical disaster points in the coastal slope of the river flowing into the sea and the spatial information of the corresponding disaster points; the investigation method includes literature review, site reconnaissance and remote sensing interpretation, wherein the literature review range is local statistical yearbook, news reports and published papers, site reconnaissance adopts total station and unmanned aerial oblique photogrammetry, remote sensing interpretation adopts object-oriented classification method and SBAS-InSAR technology.

[0022] Step S12: Obtain the number of slopes with collapse danger in the coastal slope of the river flowing into the sea and the spatial information of the corresponding slope, and obtain the riverbed width , riverbed depth , angle between collapse body migration direction and river , vertical height from normal water surface to bottom end of collapse body and vertical height of collapse body ;

[0023] Calculate the riverbed cross-section area at each slope , and the calculation formula is as follows:

[0024] ;

[0025] Calculate the volume of potential collapse body , and the specific method is: determine the existing cracks of each slope through site reconnaissance, draw the through crack surface, respectively estimate the longest distance of the rear edge crack to the front edge free surface on the drawn through crack surface , the longest distance of the two side cracks on the drawn through crack surface , the length of the rear edge crack to the front edge free surface on the drawn through crack surface that has appeared , the length of the two side cracks on the drawn through crack surface that has appeared , calculate the volume of rock-soil body between the through crack surface and the free surface through the estimated , , and , and the estimated rock-soil volume is the volume of potential collapse body ;

[0026] Step S13: Randomly select a slope without disaster signs at from the slope at , and together with the historical disaster point at , form a collapse potential degree calculation data set, and the collapse potential degree calculation data set includes samples;

[0027] Step S14: determining collapse potential degree preliminary factors, eliminating the correlation and multicollinearity based on Pearson correlation coefficient method and variance inflation coefficient method respectively, and defining the preliminary factors passing the correlation and multicollinearity test as risk factors. d The values of the risk factors of each sample are determined and scaled.

[0028] Preferably, step S14 is specifically as follows:

[0029] The collapse potential degree preliminary factors include elevation, slope, aspect, joint density, slope type, slope structure, plan curvature, profile curvature, SPI (stream power index), STI (terrain wetness index), lithology, river distance, road distance, NDVI (normalized difference vegetation index), fault distance and TWI (topographic wetness index).

[0030] The Pearson correlation coefficient calculation formula is as follows:

[0031] ;

[0032] Wherein: is the Pearson correlation coefficient of the preliminary factor and the preliminary factor , and are the mean values of the preliminary factor and the preliminary factor , and are the variances of the preliminary factor and the preliminary factor , is the covariance of the preliminary factor and the preliminary factor , E is the expectation, and if the Pearson correlation coefficient of a preliminary factor with no less than two other preliminary factors is greater than 0.4 or not greater than -0.4, the preliminary factor is deleted;

[0033] The variance inflation coefficient calculation formula is as follows:

[0034] ;

[0035] Wherein: is the variance inflation coefficient of the preliminary factor , is the multiple correlation coefficient of the preliminary factor for regression analysis of the remaining preliminary factors, and if the variance inflation coefficient of a preliminary factor is greater than 10, the preliminary factor is deleted;

[0036] The risk factors of each sample are scaled by a vector method, and the scaling vector includes d +1 elements, the first d elements are input items, and the values of each risk factor are sequentially taken, and the d +1 element is an output item, wherein The value of the historical disaster point is 1, The value of the slope without disaster signs is 0.

[0037] Preferably, step S2 is specifically as follows:

[0038] Step S21: divide the samples into a training set and a validation set according to a set proportion, train and verify a plurality of mathematical models through the training set and the validation set, and calculate the goodness index and the accuracy of the trained mathematical models;

[0039] The goodness index calculation method is as follows:

[0040] ;

[0041] Wherein: is the goodness index of the mathematical model, and are the total positive rate and the accuracy of the mathematical model, respectively;

[0042] The total positive rate and the accuracy calculation formula are as follows:

[0043] ;

[0044] ;

[0045] Wherein: is the number of samples that have collapsed and are predicted to collapse, is the number of samples that have not collapsed but are predicted to collapse, is the number of samples that have collapsed but are predicted to be non-collapsed;

[0046] Step S22: determine the mathematical model with the maximum goodness index as the best mathematical model, if the goodness indexes of no less than two mathematical models are the same and maximum, calculate the AUC values of the above no less than two mathematical models, select the one with the maximum AUC as the best mathematical model, if the AUCs of no less than two mathematical models are the same and maximum, select the one with the shortest ROC curve drawing time from the above no less than two mathematical models as the best mathematical model;

[0047] Step S23: calculate the collapse potential degree of the slope at by the selected best mathematical model .

[0048] Preferably, the mathematical models include the analytic hierarchy process (AHP) model, the deterministic coefficient model, the maximum-minimum hill-climbing model, the interpretable neural network model, the C5.0 decision tree model, the ID4.5 decision tree model, the maximum entropy model, the feedforward neural network model, the recurrent neural network model, the information content-random forest model, the AHP-information content model, the particle swarm optimization-support vector machine model, and the deterministic coefficient-random forest model.

[0049] Preferably, in step S3, the formula for calculating the typhoon rainfall impact coefficient is as follows:

[0050] ;

[0051] in: The formula for predicting daily available rainfall is as follows:

[0052] ;

[0053] in: This represents the typhoon's rainfall on that day. For the front The amount of rainfall during a typhoon is determined based on weather forecasts or actual rainfall measurements.

[0054] The rainfall threshold for landslide initiation is calculated as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Number of rain stations within the designated area of ​​the slope ,calculate Available rainfall at various rain gauge stations on the days when historical disasters occurred on the slope. The minimum available rainfall was determined using the Glikin method. Interpolation is performed to obtain The threshold for triggering rainfall is determined by the slope collapse.

[0055] Preferably, in step S3, the seawater intrusion enhancement coefficient during the collapse is... The calculation formula is as follows:

[0056] ;

[0057] in: The adjustment coefficient for river segment type is 1.25 for canyon meandering river segments, 1.00 for canyon straight river segments and open meandering river segments, and 0.75 for open straight river segments. The classification of river segment type shall be carried out in accordance with the relevant provisions of the "Specifications for Hydrological Survey and Design of Highway Engineering" (JTGC30-2015) and the "Specifications for Survey and Design of Highway Bridge Sites" (JTJ 062-91). This represents the estimated rise in water level at each slope due to seawater intrusion on the predicted date.

[0058] Preferably, in step S3, the crack status enhancement coefficient of the collapse occurrence The calculation formula is as follows:

[0059] ;

[0060] Wherein: is the collapse body size adjustment coefficient, which is related to the potential collapse body volume, and the value method is shown in Table 1.

[0061] Table 1 Value method of collapse body size adjustment coefficient

[0062] ;

[0063] is the crack length of the rear edge crack to the front edge free face that has appeared on the depicted through crack surface, is the crack length of the two side cracks that has appeared on the depicted through crack surface, is the longest distance of the rear edge crack to the front edge free face on the depicted through crack surface, is the longest distance of the two side cracks on the depicted through crack surface.

[0064] According to the shortest distance of the collapse body front edge to the river bank And the ground slope Determine , the determination method is as follows:

[0065] When 0≤ α < 35°:

[0066] ;

[0067] When 35°≤ α < 55°:

[0068] ;

[0069] When 55°≤ α < 75°:

[0070] ;

[0071] When 75°≤ α ≤ 90°:

[0072] .

[0073] Preferably, in step S5, the dam data and river data in the historical river blocking dam disaster are counted, the dam data includes the dam length , the river data includes the flow velocity , the river bed width river bed depth and cross-sectional area and normalized river data , , and The normalization method is as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] wherein: and are the minimum and maximum values of the flow velocity, respectively;

[0079] and are the minimum and maximum values of the river bed width, respectively;

[0080] and are the minimum and maximum values of the river bed depth, respectively;

[0081] and are the minimum and maximum values of the river bed cross-sectional area, respectively;

[0082] and the function relationship between the dam data and the normalized river data is solved, and the corresponding formula is as follows:

[0083] ;

[0084] wherein, , , , , , , , , , , , , , and are regression coefficients;

[0085] The function relationship after solving is used to calculate The volume of collapse required for the slope to block the river and form a dam is calculated by the following formula:

[0086] ;

[0087] Wherein: The volume of collapse required for the river to be blocked and form a dam, The cross-sectional area of the river bed, The length of the dam body.

[0088] Preferably, the median value of the is used to determine the early warning level, early warning strategy and early warning measures, as shown in Table 2.

[0089] Table 2 Relationship between early warning level, early warning strategy and early warning measures

[0090] ;

[0091] Therefore, the present application adopts the above-mentioned typhoon and seawater backflow comprehensive induced collapse blocking river and dam prediction and early warning method, which has the beneficial effects of:

[0092] (1) The selection of the potential degree hazard factor takes into account the particularity of typhoon and seawater backflow comprehensive induced collapse, and the correlation and multicollinearity are eliminated based on the Pearson correlation coefficient method and the variance inflation coefficient method, which not only reflects the original information of the disaster environment, but also reduces the calculation complexity. At the same time, the present application uses no less than 13 kinds of mathematical models to calculate the collapse potential degree and selects the best model based on multiple indexes, which greatly improves the accuracy of the calculation results of the potential degree.

[0093] (2) In the calculation of the typhoon and seawater backflow comprehensive induced collapse occurrence probability, the collapse potential degree, the typhoon rainfall influence coefficient, the seawater backflow inundation enhancement coefficient and the crack status enhancement coefficient are introduced, which comprehensively considers various inducing factors of the disaster environment, and is more in line with the disaster chain mechanism and evolution mechanism of "typhoon→collapse→block river and form dam".

[0094] (3) Due to the influence of the underlying surface factor, the collapse occurs later than the rainfall process, and the process rainfall and the landing day rainfall cannot reflect the infiltration mechanism of the typhoon rainfall. The present application can accurately reflect the inducing effect of the typhoon rainfall on the collapse by using the available rainfall to represent the time sequence characteristics of the typhoon rainfall.

[0095] (4) The present application improves the accuracy of typhoon and seawater backflow comprehensive induced collapse prediction by introducing the crack status enhancement coefficient, and more accurately describes the influence of the underlying surface factor on the collapse.

[0096] ​(5) In order to accurately depict the uncertainty of the volume of the collapse body that flows into the river, the present application adopts a probability method to reveal the "node" information of the "typhoon -> collapse -> river blocking dam" disaster chain, and sets that it obeys the Laplace distribution with the mean value and the variance of 2, so as to overcome the shortage that the discrete parameter cannot be described when the deterministic method is used to reveal the evolution mechanism of the disaster chain. θ k 4 V The mean value and the variance of 2 are used to overcome the shortage that the discrete parameter cannot be described when the deterministic method is used to reveal the evolution mechanism of the disaster chain.

[0097] The technical solutions of the present application will be further described in detail below by means of the drawings and the embodiments. DESCRIPTION OF DRAWINGS

[0098] Figure 1 It is a flow chart of the typhoon and seawater backflow comprehensive induced collapse river blocking dam prediction and early warning method of the present application. DETAILED DESCRIPTION

[0099] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0100] EMBODIMENT

[0101] This embodiment is aimed at the Fufang River, which has a total length of 71.8 km, a watershed area of 1040 km 2 , a mean annual precipitation of 923.5 mm, an annual runoff depth of 347.8 mm, a corresponding annual runoff of 362 million m 3 , and a river network density of 0.37 km / km 2 . The estuary is located in the southeast of Jiacangsi Village, Kuishan Street, Rizhao Economic Development Zone, Shandong Province, with a mouth width of 800 m. The Fufang River basin experiences 3.7 typhoons per year on average, and the typhoons are accompanied by seawater backflow. The terrain along the coast is rugged and complex, and geological disasters such as collapse develop, which provides natural conditions for the formation and evolution of the "typhoon -> collapse -> river blocking dam" disaster chain.

[0102] As shown in the figure, step S1: acquiring the spatial information and attribute information of the slope of the river flowing into the sea; constructing a collapse potential degree calculation data set, determining the collapse potential degree hazard factors and performing scaling. Figure 1 In the Fufang River and the slope along the coast:

[0103] The number of historical disaster points

[0104] = 206; The longitude and latitude are between:

[0105]

[0106] ​​35°16'52.07"N ~ 35°43'09.51"N;

[0107] 119°18'03.24"E ~ 119°28'35.10"E.

[0108] Literature review: download the corresponding data from the China Statistical Database. In this example, the downloaded data is the Rizhao Statistical Yearbook. In addition, 17 news reports and 25 papers were consulted.

[0109] Field reconnaissance: the total station used was Topcon GTS2002, and the unmanned aerial vehicle was DJI Mavic 3.

[0110] Remote sensing interpretation: object-oriented classification method was used to analyze Landsat 8 satellite images to identify disaster hazard areas. SBAS-InSAR technology was used to process Sentinel-1 satellite images to obtain ground subsidence data in the hazard area.

[0111] Number of slopes with collapse risk = 533;

[0112] The latitude and longitude are between:

[0113] 35°16'30.49"N ~ 35°43'46.18"N;

[0114] 119°17'57.64"E ~ 119°28'01.74"E;

[0115] River bed width The maximum and minimum values are as follows: B min = 83.15m, B max = 746.98m;

[0116] River bed depth The maximum and minimum values are as follows: h min = 5.23m, h max = 17.63m;

[0117] Angle between collapse body migration direction and river The maximum and minimum values are as follows: θ min = 0, θ max = 90°;

[0118] Vertical height from normal water surface to bottom of collapse body The maximum and minimum values are as follows: h 1,min = 0.95m,h 1,max = 7.10 m;

[0119] Vertical height of collapse body The maximum and minimum values are as follows: h 2,min = 1.35 m, h 2,max = 27.95 m;

[0120] Riverbed cross-sectional area S The maximum and minimum values are as follows: S min = 684 m 2 , S max = 9467 m 2 ;

[0121] Potential collapse body volume The maximum and minimum values are as follows: V min = 831 m 3 , V max = 135246 m 3 ;

[0122] The maximum and minimum values of the crack-related dimensions are as follows:

[0123] l h,min = 2.35 m ，l h,max = 97.40 m, l c,min = 1.80 m, l c,max = 126.05 m, l x,h,min = 0, l x,h,max = 38.15 m, l x,c,min = 0, l x,c,max = 45.20 m.

[0124] Randomly selected 206 slopes from 533 slopes without disaster signs, together with 206 historical disaster points, constitute a collapse potential degree calculation data set, including 412 samples. The Pearson correlation coefficients of slope type, profile curvature and road distance are 0.427, -0.509, respectively. The Pearson correlation coefficients of other arbitrary two primary selection factors are all greater than -0.4 and less than 0.4, and the VIF of TWI is 11.024.

[0125] Therefore, the slope type and TWI are deleted, and a total of d= 14 risk factors, as shown in Table 3.

[0126] Table 3 14 risk factors obtained

[0127] ;

[0128] The risk factors of 412 samples are scaled by using the vector method: the scale vector of each sample includes 15 elements, the first 14 elements are input items, and the values of each risk factor are taken in turn, and the 15th element is an output item, wherein the value of 206 historical disaster points is 1, and the value of 206 slopes without disaster signs is 0. The risk factor scaling method is shown in Table 4.

[0129] Table 4 Risk factor scaling

[0130] ;

[0131] Step S2: determining a collapse potential degree calculation model to calculate the collapse potential degree of each slope. The 412 samples are evenly divided into m = 4 parts, each with 103 samples, 3 parts (309) are randomly selected as training samples, and 1 part (103) is selected as a validation sample. Based on the training sample, the selected 13 mathematical models are trained, and the goodness index of each model to the validation sample is calculated, and the calculation result is shown in Table 5.

[0132] Table 5 Goodness index of each model

[0133] ;

[0134] The goodness index of the interpretable neural network model (DNN) and the coefficient of determination-random forest model (CF-RF) is the largest and the same, which is 0.942, and the output values of the two models are: TP = 48, FP = 4, TN = 49, FN = 2, TPR = 0.960, Precision = 0.923. The AUC of the above two models is calculated again, and the result shows that the AUC of the interpretable neural network model is 0.912, and the AUC of the coefficient of determination-random forest model is 0.915, therefore, the coefficient of determination-random forest model is selected as the best model. The collapse potential degree of the slope at 533 is calculated by using the model , and the maximum value of the calculation result is: k 0,min = 0.021, k 0,max = 0.967.

[0135] Step S3: Calculate the typhoon rainfall influence coefficient, seawater backflow inundation enhancement coefficient and crack status enhancement coefficient of each slope collapse occurrence.

[0136] The number of rainfall stations within 30 km of the slope at distance 533 =8. Investigate the available rainfall of each rainfall station on the day of the historical disaster occurrence Q k And statistics Q k,min The results show that:

[0137] Jiacang estuary monitoring station Q k,min = 182.64 mm, Xihaiya monitoring station Q k,min =173.24 mm, Gaojia village monitoring station Q k,min =175.18 mm, Xiaoguzhen west monitoring station Q k,min =186.27 mm, Caijia mudflat monitoring station Q k,min =199.52 mm, Pingtailing monitoring station Q k,min =196.25 mm, Feijia village monitoring station Q k,min =185.28 mm, Dongsun village monitoring station Q k,min =186.37 mm.

[0138] Based on the Gringold interpolation method of ArcGIS 10.2 spatial analysis function, the collapse starting rainfall threshold of 533 slopes at 8 rainfall stations Q k,min is obtained by interpolation Q y The results show that: Q y,min =170.13 mm, Q y,max =205.08 mm.

[0139] Taking Typhoon No. 3 “Goni” affecting Rizhao City, Shandong Province in 2024 as an example, the typhoon rainfall influence coefficient of collapse occurrence k 1, the results show that: k 1,min =0.165, k 1,max =0.581.

[0140] According to engineering experience and meteorological forecast, the water level rise value Δ caused by seawater backflow at each slope is estimatedh For example, typhoon "Goni", the value of Δ h min =0, Δ h max =0.65m. The type of river section at each slope is determined by field investigation, and the results show that there are 63 canyon curved river sections, 96 canyon straight river sections, 143 open curved river sections, and 231 open straight river sections. The sea water backflow inundation enhancement coefficient of collapse at each slope is calculated for example of typhoon "Goni", and the results show that: k 2,min =0, k 2,max =0.119.

[0141] Among the 533 slopes, V ≤1000m 3 of 291, 1000m 3 < V ≤10000m 3 of 99, 10000m 3 < V ≤100000m 3 of 82, V >100000m 3 of 61, that is, 291 slopes of g =0.4, 99 slopes of g =0.6, 82 slopes of g =0.8, and 61 slopes of g =1.0. The crack status enhancement coefficient of collapse occurrence at each slope is calculated k 3, and the results show that: k 3,min =0, k 3,max =0.635.

[0142] Step S4: Calculate the collapse occurrence probability induced by typhoon and sea water backflow.

[0143] The collapse occurrence probability induced by typhoon and sea water backflow at each slope is calculated, and the results show that: p l,min =0, p l,max =0.634.

[0144] Step S5: Calculate the damming river and dam occurrence probability induced by typhoon and sea water backflow at each slope.

[0145] The dam body data and river data in the history of damming river and dam disasters are counted, and the 、 of the 249 occurred damming river and dam disasters are obtained. , and data, and normalization processing to obtain , , and , calculated and , , and function relationship as follows:

[0146] .

[0147] Calculate the length of the dam body of the slope at 533 and the volume of debris flow required to occur blocking river dam V g , the results show: L b,min = 3.94 m, L b,max = 15.27 m, V g,min = 2987 m 3 , V g,max = 116444 m 3 .

[0148] Determine the collapse body transport loss index of the slope at 533 k 4, the results show that the slope at 147 k 4 = 1.0, the slope at 102 k 4 = 0.9, the slope at 75 k 4 = 0.8, the slope at 64 k 4 = 0.7, the slope at 52 k 4 = 0.6, the slope at 48 k 4 = 0.5, the slope at 33 k 4 = 0.4, the slope at 12 k 4 = 0.3.

[0149] Calculate the average volume of the collapse body that can be flushed into the river channel of the slope at 533, the results show that the minimum value is 1107 m 3 , the maximum value is 135724 m 3 . Taking Typhoon "Gomei" as an example, the comprehensive induced collapse blocking river dam occurrence probability of each slope by typhoon and seawater backflow is calculated, the results show that: p cb,min = 0, p cb,max = 0.602.

[0150] Step S6: According to the probability of collapse damming induced by typhoon and seawater backflow, the warning is graded.

[0151] Because the slope at 533 is p cb Median p cb,m = 0.382, the yellow warning of collapse damming induced by typhoon and seawater backflow should be started, and the warning strategy and measures are shown in Table 6.

[0152] Table 6 Warning grade, warning strategy and warning measures of the embodiment

[0153] ;

[0154] Based on the above method and ArcGIS Engine, the prediction and warning system of collapse damming induced by typhoon and seawater backflow along the coast of the river into the sea is developed, which can realize the real-time map query function of warning grade, warning strategy and warning measures, and can also query all spatial information and attribute information of Futeng River, 206 historical disaster points, 533 slopes and 8 rainfall stations. The spatial information includes the river distribution direction and the longitude and latitude of the historical disaster points, slopes and rainfall stations, and the attribute information includes the river section type, n 1、 n 2、 B 、 h 、 θ 、 h 1、 h 2、 S 、 V 、 l h 、 l c 、 l x,h 、 l x,c 、 ρ i,j 、 u i 、 u j 、 σ i 、 σ j 、cov( i , j )、 VIF i 、 d 、 m 、 TP 、 FP 、 FN 、 TN 、 k 0、 Y 、TPR , Precision, AUC, FPR , n 3, Q k , Q k,min , Q y , Q y,k , k 1, Q 0, Q i , Delta h , k 2, η , k 3, p l , k 3, g , L b , v b , B y , h y , S y , v b,1 , B y,1 , h y,1 , S y,1 , V g , v b,min , B y,min , h y,min , S y,min , v b,max , B y,max , h y,max , S y,max , t 0, t 1, t 2, t 3, t 4, t 5, t 6, t 7, t 8, t 9, t 10 ,t 11 、 t 12 、 t 13 、 t 14 、 L q 、 α 、 k 4、 p cb 、 p cb,m .

[0155] Therefore, the typhoon and seawater backflow comprehensive induced collapse damming river to form dam prediction and early warning method is adopted, the warning level is divided into blue warning, yellow warning, orange warning and red warning four levels, the mutual feedback mechanism of collapse occurrence and potential degree, typhoon rainfall, seawater backflow and crack development and other disaster environment characteristics can be revealed, and it is connected with disaster risk assessment and monitoring in front and connected with engineering treatment measure decision-making in back, the personnel casualties and economic losses caused by "typhoon→collapse→damming river to form dam" disaster chain are greatly reduced, and it is helpful to form a perfect disaster risk prevention and control system.

[0156] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application rather than limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: the technical solutions of the present application can still be modified or replaced by the equivalent, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A typhoon and seawater backflow comprehensive induced collapse damming river damming prediction and early warning method, characterized in that, The specific steps are as follows: Step S1: obtaining the spatial information and attribute information of the river slope into the sea, constructing the collapse potential degree calculation data set, determining the collapse potential degree hazard factor and scaling; Step S2: determining the calculation model of the collapse potential degree, calculating the collapse potential degree of each slope; Step S3: calculating the typhoon rainfall influence coefficient of the collapse of each slope, the seawater backflow inundation enhancement coefficient and the crack status enhancement coefficient; Step S4: calculating the typhoon and seawater backflow comprehensive induced collapse occurrence probability, and the calculation formula is as follows: ； wherein: is the probability of collapse induced by typhoon and seawater intrusion, is the potential degree of slope collapse, is the rainfall influence coefficient of typhoon, is the inundation enhancement coefficient of seawater intrusion, is the crack status enhancement coefficient; Step S5: Assuming that the volume of the collapse body capable of rushing into the river channel obeys the sin Step S6: grading early warning according to the typhoon and seawater backflow comprehensive induced collapse blocking river dam occurrence probability. k 4 V Laplace distribution with mean and variance of 2, the probability of the occurrence of the typhoon and seawater backflow induced collapse blocking river and damming of each slope is calculated:​ ； wherein: is the probability of the collapse blocking the river and forming a dam induced by typhoon and seawater intrusion, is the cross-sectional area of the river bed at the slope, is the length of the dam body, is the loss index of the collapse body during transportation, is the volume of the potential collapse body, is the angle between the transportation direction of the collapse body and the river. Step S1 is specifically as follows:

2. The typhoon and seawater backflow induced collapse damming method according to claim 1, characterized in that, Step S14 is specifically: Step S11: Obtain the number of historical disaster points of the coast slope of the river flowing into the sea and the spatial information of the corresponding disaster points; Step S12: obtaining the number of slopes with collapse risk in the coastal slope of the river flowing into the sea and the spatial information of the corresponding slope, and obtaining the riverbed width , the riverbed depth , the angle between the collapse body migration direction and the river , the vertical height from the normal water surface to the bottom end of the collapse body , and the vertical height of the collapse body ; The cross-sectional area of the riverbed at each slope is calculated The calculation formula is as follows: ； Calculate the potential collapse volume The specific method is: through the site reconnaissance to determine the existing cracks of each side slope, draw the through crack surface, respectively estimate the longest distance of the rear edge crack to the front edge free face on the drawn through crack surface The longest distance of the two side cracks on the drawn through crack surface The length of the crack of the rear edge crack to the front edge free face on the drawn through crack surface The length of the crack of the two side cracks on the drawn through crack surface Through the estimated 、 、 And Calculate the rock-soil volume between the through crack surface and the free surface, and the estimated rock-soil volume is the potential collapse volume ; Step S13: from randomly selected from the slope without disaster signs, and historical disaster points together constitute a collapse potential degree calculation dataset, the collapse potential degree calculation dataset includes samples; Step S14: Determine the collapse potential degree preliminary selection factor, eliminate its correlation and multicollinearity based on Pearson correlation coefficient method and variance inflation coefficient method respectively, and define the preliminary selection factor passing the correlation and multicollinearity test as a risk factor. d The risk factor is defined as a risk factor, and the value of each sample is determined and scaled.

3. The typhoon and seawater backflow induced collapse damming method according to claim 2, characterized in that, The initial selection factors of the collapse potential degree include elevation, slope, slope direction, joint density, slope type, slope structure, plane curvature, profile curvature, water flow power index, terrain humidity index, lithology, river distance, road distance, uniform vegetation coverage index, fault distance and surface runoff accumulation; The Pearson correlation coefficient calculation formula is as follows: The variance inflation coefficient calculation formula is as follows: ； wherein: is the Pearson correlation coefficient of the primary factors and is the mean of the primary factors and is the variance of the primary factors and is the covariance of the primary factors is the Pearson correlation coefficient of the primary factors and is the mean of the primary factors and is the variance of the primary factors and is the covariance of the primary factors E is the expectation that a primary factor is deleted if its Pearson correlation coefficient with not less than two other primary factors is > 0.4 or < -0.

4. Step S2 is specifically as follows: ； in: Preliminary selection factors The variance inflation coefficient, Preliminary selection factors For the remaining initial factors, perform regression analysis on the multiple correlation coefficients. If the variance inflation coefficient of the initial factors is greater than 10, then delete the initial factors. The vector method is used to scale the risk factors of each sample, and the scaling vector includes d +1 elements, the first d elements are input items, and the values of each risk factor are sequentially arranged, and the d +1 element is an output item, wherein the value of the historical disaster point is 1, the value of the slope without disaster signs is 0.

4. The typhoon and seawater backflow induced collapse damming method according to claim 2, characterized in that, The calculation method of the goodness index is as follows: Step S21: divide the samples into a training set and a validation set according to a set ratio, train and verify a plurality of mathematical models through the training set and the validation set, and calculate the goodness index and the accuracy of the trained mathematical models. Step S21: divide the samples into a training set and a validation set according to a set ratio, train and verify a plurality of mathematical models through the training set and the validation set, and calculate the goodness index and the accuracy of the trained mathematical models. The total positive rate and accuracy calculation formula is as follows: ； wherein: is the goodness index of the th mathematical model, and are the total positive rate and accuracy of the th mathematical model, respectively. Step S22: determining the mathematical model with the maximum goodness index as the best mathematical model, if the goodness indexes of no less than two mathematical models are the same and maximum, calculating the AUC values of the above no less than two mathematical models, selecting the one with the maximum AUC as the best mathematical model, if the AUCs of no less than two mathematical models are the same and maximum, selecting the one with the shortest ROC curve drawing time from the above no less than two mathematical models as the best mathematical model; ； ； wherein: is the number of samples that have collapsed and are predicted to collapse, is the number of samples that have not collapsed but are predicted to collapse, is the number of samples that have collapsed but are predicted to be non-collapsed; The mathematical models include the analytic hierarchy process model, the determination coefficient model, the maximum and minimum hill climbing model, the interpretable neural network model, the C5.0 decision tree model, the ID4.5 decision tree model, the maximum entropy model, the feedforward neural network model, the recurrent neural network model, the information amount-random forest model, the analytic hierarchy process-information amount model, the particle swarm-support vector machine model and the determination coefficient-random forest model. Step S23: Calculate by the selected best mathematical model Degree of potential for slope collapse .

5. The typhoon and seawater backflow induced collapse damming method according to claim 4, characterized in that, In step S3, the typhoon rainfall influence coefficient calculation formula is as follows:

6. The typhoon and seawater backflow induced collapse damming method according to claim 1, wherein, And the function relationship of the dam body data and the normalized river data is solved, and the function relationship formula is constructed as follows: ； wherein: To predict the daily available rainfall, the following formula is used: ； wherein: is the typhoon rainfall amount of the day, is the typhoon rainfall amount of the day before, is determined according to the weather forecast or the measured rainfall amount; The rainfall threshold for landslide initiation is calculated as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Number of rain stations within the designated area of ​​the slope ,calculate Available rainfall at various rain gauge stations on the days when historical disasters occurred on the slope. The minimum available rainfall was determined using the Glikin method. Interpolation is performed to obtain The threshold for triggering rainfall is determined by the slope collapse.

7. The typhoon and seawater backflow induced collapse damming method according to claim 1, characterized in that, In step S3, the flooding enhancement factor of seawater backflow due to the collapse The calculation formula is as follows: ； wherein: is a reach type adjustment coefficient, is an estimated water level rise at each slope due to seawater intrusion on the prediction day.

8. The typhoon and seawater backflow induced collapse damming method according to claim 1, characterized in that: In step S3, the crack present state enhancement coefficient of the collapse occurrence The calculation formula is as follows: ； wherein: is a collapse body size adjustment factor, related to the potential collapse body volume; is the longest distance of the back crack to the front free face on the depicted through-going crack surface, is the longest distance of the side cracks on the depicted through-going crack surface; is the length of the back crack to the front free face on the depicted through-going crack surface that has already appeared, is the length of the side cracks on the depicted through-going crack surface that has already appeared; debris-runout loss index correlates with the shortest distance of the debris front to the river bank and the ground slope ​ when 0 < x < 90° α when 0 < x < 35° ； when 35° < a < 45° α < 55°: ； When 55° < a < 75° α < 75°. ； When 75° < a < 90°: α When 75° < a < 90°: 。 9. The typhoon and seawater backflow induced collapse damming method according to claim 1, wherein, In step S5, the dam body data including dam body length , the river data including flow velocity , riverbed width , riverbed depth and cross-sectional area in historical river-blocking dam disasters are counted, and the river data is normalized to obtain , , and . The normalization method is as follows: ； ； ； ； wherein: and are the minimum and maximum values of the flow rate, respectively; and are the minimum and maximum values of the riverbed width, respectively; and are the minimum and maximum values of the riverbed depth, respectively; and are the minimum and maximum values of the cross-sectional area of the river bed, respectively; ​ ； wherein , , , , , , , , , , , , , and are regression coefficients; The function relationship after solving is calculated The collapse volume required for the slope to block the river and form a dam is calculated by the following formula: ； wherein: Vc is the volume of the collapsed body required to dam the river, A is the cross-sectional area of the river bed, L is the length of the dam body.

10. The typhoon and seawater backflow induced collapse damming method according to claim 1, characterized in that: According to the median value determine the early warning level, early warning strategy and early warning measures, the early warning level is set to 4, and each early warning level corresponds to a corresponding early warning strategy and early warning measures.

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