CN114236095A - Mountain expressway rainfall induced landslide regional grading early warning method - Google Patents
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Abstract
The application discloses a mountain highway rainfall induced landslide subarea grading early warning method, which relates to the technical field of landslide hazard early warning, and is characterized in that a TRIGRS model is utilized to simulate the transient pore water pressure change of each grid unit in a research area along the highway where rainfall is easy to induce landslide in the rainfall infiltration process, and the stability of each grid unit is calculated; secondly, establishing a mathematical function relation between the average rainfall intensity I and the rainfall duration D and the stability of each evaluation unit by using a TRIGRS model by taking the grid unit as the evaluation unit so as to reduce the calculation cost for searching the rainfall I-D threshold value and improve the rainfall threshold value solving precision; then, solving a rainfall I-D threshold value of each grid unit in the research area to obtain the spatial distribution of the rainfall I-D threshold values; and finally, establishing a three-level meteorological early warning scheme in a research area by combining real-time rainfall data on the basis of the rainfall threshold spatial distribution map, and providing guarantee for safe operation of the highway.
Description
Technical Field
The application relates to the technical field of landslide hazard early warning, in particular to a sectional grading early warning method for landslide induced by rainfall along a mountain highway.
Background
Landslide is a common and common natural phenomenon, but due to the lack of an effective early warning system, a great amount of casualties and huge property loss are caused worldwide. Among the numerous triggers, rainfall is the most prominent factor inducing landslide. Therefore, it is one of the most common methods to establish a meteorological warning plan for a research area using rainfall thresholds (defined as rainfall conditions that meet or exceed the conditions that may trigger landslide). In order to ensure the safety of important linear projects such as expressways and the like during operation, the development of rainfall threshold estimation and meteorological early warning research of rainfall induced landslide along the highway has important theoretical and practical significance.
Linear engineering such as expressways and the like has the characteristic of extending, and the landform, the geological condition, the rock-soil body characteristic and the hydrological condition along the line have certain differences; in addition, linear projects such as expressways are mainly threatened by geological disasters on two sides of the line, so that only areas within a certain range (such as 500-1000 m) on two sides of the line need to be evaluated, and the research area has the characteristic of small area in the direction perpendicular to the line. Based on the characteristics of the research area, obtaining an area threshold value by using a traditional empirical method may not be accurate, and few researches on the rainfall threshold value estimation and meteorological early warning of the expressway are carried out at present, so that the research area is lack of the attempts.
Disclosure of Invention
The embodiment of the application provides a sectional grading early warning method for rainfall-induced landslide along a mountain highway, which aims to perform rainfall threshold estimation and weather early warning of rainfall-induced landslide along the highway and provide guarantee for safe operation of the highway.
In order to solve the above problems, the embodiment of the application discloses a sectional grading early warning method for rainfall-induced landslide along a mountain highway, which comprises the following steps:
step S1: determining a research area where rainfall along a highway is easy to induce landslide, generating a plurality of training samples in the sampling range of the average rainfall intensity and the rainfall duration of the research area, and inputting each training sample into a TRIGRS model, wherein the TRIGRS model is used for simulating the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process so as to output and obtain a stability coefficient of each grid unit;
step S2: training each training sample and the stability coefficient of each grid unit corresponding to the training sample by using an LSSVM (least squares support vector machine), and establishing a stability discrimination model of each grid unit; the stability discrimination model is used for representing a mathematical function relation between the average rainfall intensity and the rainfall duration and the stability of each grid unit;
step S3: calculating an average rainfall intensity-rainfall duration threshold value of each grid unit in the research area according to the stability discrimination model of each grid unit, and obtaining the spatial distribution of the average rainfall intensity-rainfall duration threshold value in the research area;
step S4: and establishing a meteorological early warning scheme for the rainfall-induced landslide in the research area according to the spatial distribution of the average rainfall intensity-rainfall duration threshold value in the research area and the rainfall data obtained in real time at present.
Further, in step S1, the step of using the TRIGRS model to simulate a transient pore water pressure change of each grid cell in the research area during rainfall infiltration to output a stability coefficient of each grid cell includes:
substep S1-1: simulating the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process by using a linear solving form of a Ricketts equation:
(1) where θ is a volume water content of each grid cell, δ is a gradient of each grid cell, and K (ψ) is a hydraulic transfer function of a soil body, where θ and K (ψ) are expressed as follows, respectively, in accordance with a pressure head ψ of each grid cell:
θ＝θr+(θs-θr)exp(α'ψ*) (2)，
K(ψ)＝Ksexp(α'ψ*) (3)，
(2) in the formula (3), KsIs the saturation permeability coefficient of the soil body, thetarIs the residual volume water content, θsIs the saturated volume water content, alpha' is the Gardner parameter,. phi. phi. -phi0，ψ0Is a constant equal to 0 or-1/α';
substep S1-2: according to the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process, the TRIGRS model calculates the stability coefficient FS of each grid unit by using one-dimensional infinite slope stability:
wherein the content of the first and second substances,
(4) wherein c' is the effective cohesive force of the soil body,
is the effective internal friction angle, gamma, of the soil masswIs the gravity of water, gammasIs the gravity of the soil body; in the TRIGRS model, the pressure head ψ is a function of the groundwater level depth Z and time t of the investigation region;
wherein FS >1 represents that the slope corresponding to the grid cell is stable, FS ═ 1 represents that the slope corresponding to the grid cell is in a limit equilibrium state, and FS <1 represents that the slope corresponding to the grid cell is unstable.
Further, in the TRIGRS model, the groundwater level depth Z is expressed as a constant percentage of the soil thickness of the study area; the method further comprises the following steps:
determining the soil thickness d of each grid cell in the study area using a soil thickness versus slope relationshipsWherein:
in the formula, zmaxAnd zminMaximum and minimum values of the thickness of the soil in the investigation region, respectively, delta being the slope of each grid cell, deltamaxAnd deltaminThe maximum and minimum values of the gradient of the study area are respectively.
Further, the step S3 includes:
substeps ofStep S3-1: aiming at the stability discrimination model of each grid unit, Matlab is utilized to fix the rainfall duration D, the average rainfall intensity I is increased from small to large, and the stability discrimination model is used for the first occurrence of FS<1 is the critical condition of the grid unit, the average rainfall intensity corresponding to the grid unit reaching the critical condition is obtained, and the average rainfall intensity corresponding to the grid unit reaching the critical condition is taken as the critical rainfall intensity I under the rainfall duration Dc；
Substep S3-2: changing the rainfall duration D, solving the critical rainfall intensity of the grid unit under different rainfall durations to obtain Ic-a D data set;
substep S3-3: according to the formula IcA data set D, solving the slope and the intercept of the grid unit in a rainfall threshold model by using least square regression, and obtaining a rainfall I-D threshold curve of the grid unit according to the slope and the intercept;
substep S3-4: and traversing each grid unit, and drawing a spatial distribution diagram of the rainfall I-D threshold of the research area according to the rainfall I-D threshold curve of each grid unit.
Further, wherein the rainfall threshold model is:
Ic＝αDβ (5)，
(5) in the formula IcIs the critical average rainfall intensity of the power law equation in mm/h; d is the duration of rainfall in units of h; α is a scale parameter and β is a shape parameter related to the slope of the power-law curve;
by using the base 10 logarithm for equation (5), the equation is expressed as:
logIc＝βlogD+logα (6)，
in which beta is logIc-the slope of the logD line, representing the slope of the grid cell in the rainfall threshold model, and Log α being the intersection with the ordinate axis, representing the intercept of the grid cell in the rainfall threshold model.
Further, in the rainfall threshold model, an upper bound D of rainfall duration DmaxAt 200h, duration of rainfall DminTo search for the minimum duration of rainfall for the critical rainfall intensity.
Further, the method further comprises:
randomly generating a plurality of test samples by using Latin hypercube sampling;
respectively using the TRIGIS model and a stability discrimination model obtained by training to calculate the stability corresponding to each test sample;
and traversing all the test samples, comparing the stability calculation results of the TRIGIS model and the stability discrimination model on the same test sample, and calculating the accuracy of the stability discrimination model.
Further, the meteorological early warning scheme for rainfall-induced landslide in the research area established in the step S4 is of three levels, including:
if the average rainfall intensity of a certain rainfall event in any grid unit of the research area reaches the critical rainfall intensity of the grid unit, determining that the early warning level is first grade; wherein, under one-level early warning level, the meteorological early warning scheme includes: immediately issuing early warning to remind passing vehicles, immediately organizing workers to patrol sections corresponding to the first-level early warning level, immediately closing the expressway if dangerous cases are found, and recovering operation after the dangerous cases are eliminated;
if the average rainfall intensity of a certain rainfall event in any grid unit of the research area reaches 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is the second level; wherein, under the second grade early warning level, the meteorological early warning scheme includes: issuing early warning to remind passing vehicles and pay close attention to the change of rainfall intensity, and immediately updating and issuing the early warning level when the rainfall intensity reaches the first-level early warning level;
if the average rainfall intensity of a rainfall event in any grid unit of the research area is less than 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is three levels; wherein, under tertiary early warning level, the meteorological early warning scheme includes: and not issuing early warning, but paying attention to the change of rainfall intensity in real time and updating the early warning level in real time.
The embodiment of the application has the following advantages:
in the method, firstly, a research area where rainfall along a highway easily induces landslide is determined, rainfall threshold estimation and meteorological early warning are taken as targets, a TRIGRS model is utilized to simulate the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process, and the stability of each grid unit is calculated; secondly, establishing a mathematical function relation between the average rainfall intensity I and the rainfall duration D and the stability (stable or unstable) of each evaluation unit by using a TRIGRS model by taking the grid unit as the evaluation unit so as to reduce the calculation cost for searching the rainfall I-D threshold value and improve the precision of rainfall threshold value solving; then, solving a rainfall I-D threshold value of each grid unit in the research area to obtain the spatial distribution of the rainfall I-D threshold values; and finally, establishing a three-level weather early warning scheme in the research area by combining real-time rainfall data on the basis of the rainfall threshold spatial distribution map. Therefore, the rainfall threshold estimation and the weather early warning of landslide induced by rainfall along the highway are realized, and the weather early warning has higher rationality through relevant examples, provides guarantee for the safe operation of the highway and provides reference for establishing a weather early warning system based on the rainfall threshold for similar linear engineering.
Drawings
FIG. 1 is a flow chart illustrating steps of a sectional grading early warning method for rainfall induced landslide along a mountain highway according to the present application;
FIG. 2 is a flowchart illustrating an embodiment of the present disclosure for calculating rainfall I-D thresholds;
FIG. 3 is a schematic illustration of the location and extent of an area of interest in an example of the present application;
FIGS. 4(a) - (e) are schematic diagrams illustrating control parameters of the TRIGRS model of the research area of the present application;
FIGS. 5(a) - (C) are respectively a stability determination model of point A, point B and point C in an example of the present application, and FIG. 5(d) is a schematic diagram of the accuracy of the stability determination model of point A, point B and point C in an example of the present application;
FIGS. 6(a) -6 (c) are α, β, and D, respectively, for the rainfall threshold modelminA schematic of the impact on the early warning level;
FIGS. 7(a) - (d) show I at points A, B and C, respectivelyc-D data points and fitted rainfall I-D threshold model curve schematic;
FIGS. 8(a) - (d) show spatial distribution diagrams of critical rainfall intensity at rainfall durations of 12h, 24h, 48h and 72h, respectively;
fig. 9(a) to (c) are schematic diagrams of the area warning results of three rainfall events, respectively.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Aiming at the technical problem, the invention provides a sectional grading early warning method for rainfall-induced landslide along a mountain highway through the following embodiments, aiming at carrying out rainfall threshold estimation and meteorological early warning of rainfall-induced landslide along the highway and providing guarantee for safe operation of the highway.
Referring to fig. 1, a flow chart illustrating steps of a sectional early warning method for rainfall-induced landslide along a mountain highway according to the present application may include the following steps:
step S1: determining a research area in which rainfall along a highway is easy to induce landslide, generating a plurality of training samples in the sampling range of the average rainfall intensity and the rainfall duration of the research area, inputting each training sample into a TRIGRS (Transient rainfall infiltration and grid-based regional slope-stability model) model, wherein the TRIGRS model is used for simulating the change of Transient pore water pressure of each grid unit in the research area in the rainfall infiltration process so as to output and obtain the stability coefficient of each grid unit;
the research area is a part along the highway, and a part along the highway, which is easy to induce landslide due to rainfall, can be selected as the research area. The research area also has the characteristic of extension, and the landform, the geological condition, the rock-soil body characteristic and the hydrological condition along the line all have certain differences.
The TRIGRS model is a numerical model developed by Fortran language and used for regional slope stability evaluation, and can simulate instantaneous pore pressure change caused by rainfall infiltration and calculate the stability coefficient of each grid unit after the pore pressure change. The model can be combined with a Geographic Information System (GIS), so that the preparation of the input data of the model and the visual display of the result are facilitated.
In the present application, a plurality of training samples (generally 500-1000) can be generated in a sampling range of average rainfall intensity and rainfall duration of the research area by using uniform sampling, and then each training sample is input into the TRIGRS model, and a stability coefficient of each grid unit can be output.
The TRIGRS model mainly simulates the change of transient pore water pressure of each grid unit in a research area in the infiltration process of rainfall by using each training sample, and then the stability coefficient of each grid unit can be output, and the specific steps can comprise:
substep S1-1: simulating the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process by using a linear solving form of a Ricketts equation:
(1) where θ is a volume water content of each grid cell, δ is a gradient of each grid cell, and K (ψ) is a hydraulic transfer function of a soil body, where θ and K (ψ) are expressed as follows, respectively, in accordance with a pressure head ψ of each grid cell:
θ＝θr+(θs-θr)exp(α'ψ*) (2)，
K(ψ)＝Ksexp(α'ψ*) (3)，
(2) in the formula (3), KsIs the saturation permeability coefficient of the soil body, thetarIs the residual volume water content, θsIs the saturated volume water content, alpha' is the Gardner parameter,. phi. phi. -phi0，ψ0Is a constant equal to 0 or-1/α';
substep S1-2: according to the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process, the TRIGRS model calculates the stability coefficient FS of each grid unit by using one-dimensional infinite slope stability:
wherein the content of the first and second substances,
(4) wherein c' is the effective cohesive force of the soil body,
is the effective internal friction angle, gamma, of the soil masswIs the gravity of water, gammasIs the gravity of the soil body; in the TRIGRS model, the pressure head ψ is a function of the groundwater level depth Z and time t of the investigation region;
wherein FS >1 represents that the slope corresponding to the grid cell is stable, FS ═ 1 represents that the slope corresponding to the grid cell is in a limit equilibrium state, and FS <1 represents that the slope corresponding to the grid cell is unstable.
In an embodiment of the present application, the groundwater level depth Z is expressed as a constant percentage of the soil thickness of the study area in the TRIGRS model; the soil thickness d of each grid cell in the research area can be determined by using the relation of the soil thickness and the gradientsWherein:
in the formula, zmaxAnd zminRespectively maximum and minimum of the thickness of the soil in the investigation region, delta being for each grid cellGradient, deltamaxAnd deltaminThe maximum and minimum values of the gradient of the study area are respectively.
The remaining parameters may be determined by measurement or reference to existing literature or specifications.
Step S2: training each training sample and the stability coefficient of each grid unit corresponding to the training sample by using an LSSVM (least squares support vector machine), and establishing a stability discrimination model of each grid unit; the stability discrimination model is used for representing a mathematical function relation between the average rainfall intensity and the rainfall duration and the stability of each grid unit;
the LSSVM (Least Square SVM, Chinese name is Least Square support vector machine) belongs to a method in the prior art, and the LSSVM algorithm can be realized by calling an LS-SVMlab (V1.8) tool box developed by Suykens and the like.
Step S3: calculating an average rainfall intensity-rainfall duration threshold value of each grid unit in the research area according to the stability discrimination model of each grid unit, and obtaining the spatial distribution of the average rainfall intensity-rainfall duration threshold value in the research area;
the rainfall threshold model which is most widely used at present is a rainfall I-D threshold model, wherein the rainfall I-D threshold model is as follows:
Ic＝αDβ (5)，
(5) in the formula IcIs the critical average rainfall intensity of the power law equation in mm/h; d is the duration of rainfall in units of h; α is a scale parameter and β is a shape parameter related to the slope of the power-law curve;
by using the base 10 logarithm for equation (5), the equation is expressed as:
logIc＝βlogD+logα (6)，
in which beta is logIcThe slope of the logD line, Log α being the intersection with the ordinate axis.
It should be noted that if the rainfall threshold is solved by using the conventional method, the parameters of the TRIGRS model need to be manually and continuously adjusted to find the rainfall threshold (rainfall I-D threshold), which is very inefficient. In addition, for different rainfall durations, the TRIGRS model needs to be continuously operated to find the critical rainfall intensity when the critical condition is reached, and the calculation amount is very large. In some studies, in order to reduce the amount of calculation, when solving the critical rainfall intensity of different rainfall duration, the increment of the rainfall duration is increased or the increment of the average rainfall intensity is increased, and the measures can reduce the solving precision of the rainfall threshold. Therefore, a Least Square Support Vector Machine (LSSVM) is introduced to construct a stability discrimination model, automatic and efficient solution of the rainfall threshold value of each grid unit is realized on the basis of Matlab programming, and the detailed steps are as follows:
(1) writing an interface program between the Matlab and the TRIGRS model to realize automatic data interaction between the Matlab and the TRIGRS model: and automatically inputting the set average rainfall intensity I and the set rainfall duration D value into an input file of a TRIGRS model by using Matlab, automatically operating the TRIGRS model for calculation to obtain a stability coefficient of each grid unit in the research area, and automatically reading the stability coefficient of each grid unit into Matlab to facilitate the calculation of the rainfall threshold in the steps S2-S3.
(2) The stability discrimination model of each grid unit is established by using the LSSVM, so that the stability evaluation of the grid unit can be carried out on the basis of the LSSVM discrimination model with high calculation efficiency instead of a numerical model with large calculation amount. In the application, each training sample is brought into the TRIGRS model through the step S1, a corresponding stability coefficient can be calculated, and if the stability coefficient of a grid unit is more than or equal to 1, the grid unit is stable and is marked as '1'; if the stability factor is less than 1, the cell is unstable and is marked as '0'. In step S2, model training is performed by using an LSSVM based on each input (training sample) and each output (two classification stability discrimination results ('1' and '0') corresponding to the training sample) as a training set, so as to establish a mathematical function relationship between the average rainfall intensity and the rainfall duration and the stability of each grid unit, and thus obtain a stability discrimination model for each grid unit.
(3) After the stability discrimination model of each grid unit is established, the solution of the rainfall threshold value can be efficiently carried out based on the stability discrimination model. Based on the rainfall I-D threshold model, referring to FIG. 2, the implementation process of calculating the rainfall I-D threshold can be implemented by the following steps:
substep S3-1: aiming at the stability discrimination model of each grid unit, Matlab is utilized to fix the rainfall duration D, the average rainfall intensity I is increased from small to large, and the stability discrimination model is used for the first occurrence of FS<1 is the critical condition of the grid unit, the average rainfall intensity corresponding to the grid unit reaching the critical condition is obtained, and the average rainfall intensity corresponding to the grid unit reaching the critical condition is taken as the critical rainfall intensity I under the rainfall duration Dc；
Substep S3-2: changing the rainfall duration D, solving the critical rainfall intensity of the grid unit under different rainfall durations to obtain Ic-a D data set;
substep S3-3: according to the formula IcA data set D, solving the slope and the intercept of the grid unit in a rainfall threshold model by using least square regression, and obtaining a rainfall I-D threshold curve of the grid unit according to the slope and the intercept;
in the present application, when the least square regression is used to solve the slope and the intercept of each grid cell in the rainfall threshold model, equations (5) and (6) are used for calculation, so that the slope β of the grid cell in the rainfall threshold model and the intercept log α of the grid cell in the rainfall threshold model can be obtained. After the slope beta and the intercept Log alpha are solved, the slope beta and the intercept Log alpha are converted into a power law equation of the formula (5), and a rainfall I-D threshold curve of the grid unit can be obtained.
Substep S3-4: and traversing each grid unit, and drawing a spatial distribution diagram of the rainfall I-D threshold of the research area according to the rainfall I-D threshold curve of each grid unit.
Step S4: and establishing a meteorological early warning scheme for the rainfall-induced landslide in the research area according to the spatial distribution of the average rainfall intensity-rainfall duration threshold value in the research area and the rainfall data obtained in real time at present.
In this application, the meteorological early warning scheme of research area rainfall induction landslide of establishing can be set to three grades, includes:
if the average rainfall intensity of a certain rainfall event in any grid unit of the research area reaches the critical rainfall intensity of the grid unit, determining that the early warning level is first grade, and marking the early warning color as red; wherein, under one-level early warning level, the meteorological early warning scheme includes: immediately issuing early warning to remind passing vehicles, immediately organizing workers to patrol sections corresponding to the first-level early warning level, immediately closing the high speed if dangerous cases are found, and recovering operation after the dangerous cases are eliminated;
if the average rainfall intensity of a rainfall event in any grid unit of the research area reaches 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is the second level, and marking the early warning color as yellow; wherein, under the second grade early warning level, the meteorological early warning scheme includes: issuing early warning to remind passing vehicles and pay close attention to the change of rainfall intensity, and immediately updating and issuing the early warning level when the rainfall intensity reaches the first-level early warning level;
if the average rainfall intensity of a rainfall event in any grid unit of the research area is less than 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is three levels, and marking the early warning color as green; wherein, under tertiary early warning level, the meteorological early warning scheme includes: and not issuing early warning, but paying attention to the change of rainfall intensity in real time and updating the early warning level in real time.
Next, the implementation process and effect of the present application will be described in detail with the high-speed general construction in Yunnan province as a research object and with the rainfall threshold estimation and meteorological early warning as targets.
Overview of the study region
The general construction high-speed route is located in Tonghai county and Jianshui county in Yunnan province, and basically runs from north to south for 126 kilometers. According to the related suggestions of the geological disaster risk assessment norm (DZT 0286-2015): in the important line construction project, the evaluation range is preferably extended to 500-1000 m from two sides of the line. Thus, the area of investigation was determined to be a range of 1000m along both sides of the line, in totalThe area is 128.8km2As shown in fig. 3. The study area is in the southwest part of the cloud noble plateau, the terrain is slightly high in the northwest and low in the southeast, the direction of the mountain ranges is basically consistent with the direction of the construction line, the whole line penetrates through the construction to degrade two landform units of the low-middle mountain landform and the mountain basin (Tonghai-Quxi basin), and the ground elevation is 1262 m-2086 m. The route passes through the arc-shaped structural zone from north to south, the mountain canyon characteristics of local road sections are outstanding, and the terrain height difference is large, so that rock joint cracks in the region are relatively developed, and rock mass is broken. The main water system developed along the Yangtze river is the Yangtze river which is injected from the west to the east and belongs to the Zhujiang water system. The research area belongs to subtropical monsoon climate, the annual average temperature is 18.5 ℃, the highest temperature is 35.1 ℃, and the lowest temperature is-3 ℃. The average annual rainfall is 853mm, and the rainfall in rainy season (5-10 months) accounts for 80% of the annual rainfall. The groundwater types mainly include three types of bedrock fracture water, pore water and karst water, and the groundwater burial depth is stabilized at 0.1-15 m during exploration. The exposed stratum is changed from old to new: 1) jordan Chengjiang group (Za) siltstone and sandstone; 2) the seismic denier system lamp shadow group (Zb) is made of limestone and siltstone and sandstone; 3) fourth series (Q)4) Flushing the accumulated clay, sand and residual slope layer.
Due to the influence of rainfall, landslide disasters occur in a research area for many times, for example, 8-month and 7-day early morning in 2011, rainstorm occurs in two successive days, so that mountain landslide occurs in ant mountain sections in high-speed water-building counties, a bidirectional road is blocked by soil and stones, traffic is forcedly interrupted, and vehicles in the world are blocked; and 7, 31 months in 2017, the highway section from the sea to the ant mountain in the Yangtze river is constructed to be landslide due to rainfall, so that the highway from the sea to the ant mountain is interrupted. Most landslides in the research area occur in residual slope layers covering the upper part of bedrock, and most landslides are shallow layer landslides. Rainfall is the main causative factor in landslides in the study area: rainfall infiltration increases the water content of the soil body of the residual slope layer, the volume weight of the soil body is increased due to the increase of the water content, meanwhile, the shear strength of the soil body is obviously reduced under the action of water, the sliding resistance is reduced, and finally the landslide of the residual slope layer covered on the upper part of the bedrock is caused. Therefore, rainfall threshold estimation and weather early warning research of the general construction of high-speed rainfall induced landslide are needed to be carried out, and guarantee is provided for general construction of high-speed safe operation.
Second, model parameters
(1) Control parameters of the model
The topographic data for the area of investigation used a 12.5m x 12.5m Digital Elevation Model (DEM) provided by ASF (https:// search. ASF. alaska. edu /), as shown in FIG. 4 (a). Based on DEM data, a terrain slope map (fig. 4(b)) and a flow map (fig. 4(c)) can be generated using ArcGIS analysis. In addition, the thickness of the soil covering the upper part of the bedrock and the buried depth parameters of the underground water level are also the key for analyzing the stability of the shallow landslide. In mountain areas, the soil thickness has a greater correlation with the terrain gradient, generally, the soil thickness in a relatively gentle valley area is relatively thick, and the soil thickness in a relatively steep hill top area is relatively thin. The method adopts a relational expression between the soil thickness and the gradient provided by Baum and the like to estimate the soil thickness d of each grid unit in a research areasThe following are:
in the formula, zmaxAnd zminThe maximum value and the minimum value of the soil thickness of the research area are respectively; δ is the slope of each grid cell; deltamaxAnd deltaminMaximum and minimum values of the gradient of the study area, respectively. Based on typical longitudinal profile borehole data provided by regional survey reports, a maximum z of overburden thickness is revealedmax22.4m with a minimum value of 0 m; according to FIG. 4(c), the maximum value of the gradient δ of the investigation regionmaxIs 55 deg. and minimum value deltaminIs 0 deg.. After the above parameters are determined, the soil thickness parameter of the study area can be calculated by using equation (7), and the result is shown in fig. 4 (d). In using TRIGRS modeling, the groundwater level depth is typically estimated as a constant percentage of soil thickness. The maximum value of the groundwater level burial depth of the research area is 15m and the maximum value of the soil thickness is 22.4m, and if the ratio of the maximum values of the two is taken as the constant percentage, the groundwater level depth of the research area can be determined to be 66.96% of the soil thickness, and the result is shown in fig. 4 (e).
(2) Mechanical and hydrological parameters
The soil body on the research area is mainly the residual slope gravels (gravels)About 10 percent of the content), according to the geological survey data, the effective internal friction angle of the residual slope gravelly soil
At 25-42 deg, effective cohesive force c' is 35-55 kPa, unit weight gammasIs 27kN/m3Saturated water content thetas35% of residual water content θrThe content was 4.8%. Saturated permeability coefficient KsHydraulic diffusion coefficient D0And initial infiltration rate IZLTAre relatively more difficult and are therefore determined primarily by reference to existing literature or specifications. According to the related literature, the saturation permeability coefficient of the gravelly soil is generally 4.6 multiplied by 10-7m/s～1.0×10-4m/s, and the hydraulic diffusion coefficient is generally determined as 100 times the saturation permeability coefficient (D)0＝100Ks) Initial infiltration rate of soil layer IZLTIs generally less than D0Square of (d).
In order to obtain a combination of parameters that better conforms to the actual physical properties, the parameters are further calibrated within the empirical range of the parameters. The calibration condition one is as follows: because when the rainfall is not present, the research area does not slide, namely, under the rainfall-free condition, the whole area of the research area should be stable (the stability coefficient FS calculated by the TRIGRS model is more than 1); and (2) calibration conditions are as follows: in 2017, 31 months, a landslide occurs due to rainfall in a section from a highway to a Qujiang ant mountain which is open to the sea, and the Yuxi rainfall monitoring station closest to the landslide is inquired: before landslide occurs, the research area continuously rains for 3 days, the accumulated rainfall reaches 90mm, and the result shows that under the conditions that the average rainfall intensity is 1.25mm/h and the rainfall time is 72h, the stability FS calculated by using the TRIGRS model is less than 1 in the landslide position area, and most of other non-landslide areas are more than 1. Through repeated trial and error calibration, two calibration conditions are simultaneously met, and finally determined mechanical parameters and hydrological parameters are shown in table 1.
TABLE 1 soil mechanics and hydrologic parameters used in TRIGRS model
(3) Parameters of rainfall
The rainfall parameters in the TRIGRS model are the average rainfall intensity (I) and the rainfall duration (D). When the rainfall I-D threshold value is calculated, the average rainfall intensity searching range is set to be 0.1mm/h to 50mm/h, and the increment is 0.1 mm/h; the search range of the rainfall duration is set to be 1 h-200 h, and the increment is 2 h.
Third, results and analysis
Generating 1000 training samples by using uniform sampling, wherein the sampling range of the average rainfall intensity I is 0.1-50 mm/h, and the sampling range of the rainfall duration D is 1-200 h; secondly, calculating a stability grid file (FS is more than or equal to 1 and is recorded as '1', FS is less than 1 and is recorded as '0') of the research area corresponding to each training sample by using a TRIGIS model; finally, a stability discrimination model for each grid cell in the study area is established by an LSSVM training model based on the training samples and the stability of each corresponding grid cell (note: only 4.3% of the grid cells (36509) need to be constructed as the stability factor simulated using the TRIGRS model in 95.7% of the study area under any rainfall condition is greater than 1, i.e., 95.7% of the study area has no rainfall threshold). In order to show the calculation results, fig. 5(a) -5 (C) show the training situation of the stability discriminant model of 3 grid elements (points A, B and C), and it can be found that the LSSVM has very excellent stability classification effect for the research area. In order to check the accuracy of the stability discrimination model, the application further provides the following method:
randomly generating a plurality of test samples by using Latin hypercube sampling;
respectively using the TRIGIS model and a stability discrimination model obtained by training to calculate the stability corresponding to each test sample;
and traversing all the test samples, comparing the stability calculation results of the TRIGIS model and the stability discrimination model on the same test sample, and calculating the accuracy of the stability discrimination model.
The method and the device have the advantages that 100 test samples are additionally and randomly generated by using Latin Hypercube Sampling (LHS), and the stability condition corresponding to each test sample is calculated by using a TRIGIS model and a stability discrimination model obtained by training respectively. By comparing the results of the two, the accuracy of the stability discrimination model can be calculated, and the result is shown in fig. 5 (d). Through statistics on the accuracy of the discrimination models, it can be found that the accuracy of the stability discrimination models of all grid units can reach 97%, which indicates that the stability discrimination model of each grid unit has good precision. Therefore, the stability of each grid unit can be evaluated by using the stability discrimination model to completely replace a TRIGIS model, so that the calculation cost for subsequently searching for the rainfall threshold is obviously reduced.
Next, a rainfall threshold value of each grid cell is calculated according to the set rainfall parameters. Firstly, fixing the rainfall duration, continuously increasing the average rainfall intensity, and when the discrimination model appears '0' for the first time, the corresponding average rainfall intensity is the critical rainfall intensity of the rainfall duration; secondly, changing the rainfall duration, solving the critical rainfall intensity under different rainfall durations, and finally obtaining 100I for each grid unitc-a D data point; finally, based on these data points, the slope β and the intercept log α in each grid unit rainfall threshold model (equation 6) can be calculated by using least squares regression, so as to obtain α and β in each grid unit rainfall threshold model, and the results are shown in fig. 6(a) and 6 (b). It is also worth noting that, because it takes a certain time for the rainfall to infiltrate into the deep part of the soil, the rainfall in a short time cannot make the rainwater infiltrate into the soil sufficiently, but most of the rainwater is drained through surface runoff, so that the influence on the stability is small. In other words, in the case of short rainfall, the grid cells do not destabilize no matter how strong the rainfall is, and the critical rainfall intensity can be obtained only if the rainfall duration is sufficient to induce slope destabilization. Thus, the rainfall I-D threshold model is not applicable to any rainfall event of rainfall durationBut rather has a scope of applicability. In particular, in the rainfall threshold model, an upper bound D of rainfall duration DmaxD of 200h, rainfall duration D can be selectedminTo search for the minimum duration of rainfall for the critical rainfall intensity. The minimum rain duration (lower bound of the applicable range) for each grid cell is shown in fig. 6 (c).
FIGS. 7(a) -7 (C) show I at points A, B and C, respectivelyc-D data points and a fitted rainfall I-D threshold model. It can be found that the rainfall I-D threshold model can be well fitted with Ic-D data points, illustrating that rainfall threshold estimation using the TRIGRS model is feasible. FIG. 7(D) plots the rainfall I-D threshold curves for 36509 grid cells. It can be seen that the critical rainfall intensity of each grid cell gradually decreases as the duration of rainfall increases, but the magnitude of the change becomes smaller and smaller. The minimum critical rainfall intensity of the whole research area is 0.1mm/h, the corresponding rainfall duration is 200h, the maximum critical rainfall intensity is 39.8mm/h, and the corresponding rainfall duration is 5h, which indicates that slope instability can be caused by heavy rain in a short time or light rain in a long time. After the rainfall I-D threshold curve of each grid unit is obtained, the spatial distribution of the critical rainfall intensity of the research area in any rainfall duration within the application range can be obtained. Fig. 8 shows spatial distribution diagrams of critical rainfall intensities at rainfall durations of 12h, 24h, 48h and 72h, respectively, and it can be found that the critical rainfall intensities have large differences in space, such as a minimum value of 0.960mm/h and a maximum value of 15.079mm/h in the case of the 48h rainfall duration, and the differences are very large. This difference is reasonable because the gradient, soil thickness, etc. of each grid unit are different, and there must be a large difference in critical rainfall intensity. It is worth mentioning that the rainfall threshold based on empirical statistics only provides a single rainfall threshold for the research area, and the influence of the difference of the terrain conditions, the soil thickness and the like of different spatial positions on the rainfall threshold is not considered. Therefore, compared with a simple empirical threshold, the rainfall threshold spatial distribution given by the numerical model can reflect the difference of the terrain conditions and the soil thickness in the research areaThe effect on the rainfall threshold can also give the area where landslide is most likely to occur under different rainfall intensities.
The application takes 8 months of 2020 as an example to illustrate how rainfall thresholds may be used for early warning. There were three large rainfall events in month 8 in 2020: continuously raining for 72 hours in 31 days after 7 months to 8 months and 2 days, wherein the rainfall reaches 67.1mm, and the average rainfall intensity is 0.93 mm/h; continuously raining for 48 hours in 16 days at 8 months and 17 days at 8 months, wherein the rainfall reaches 106.6mm, and the average rainfall intensity is 2.22 mm/h; the rainfall is 12h in 29 days of 8 months, the rainfall reaches 55.3mm, and the average rainfall intensity is 4.61 mm/h. By comparing the rainfall intensity of the three rainfall events with the rainfall I-D threshold value and combining the three-level early warning scheme, the early warning results of the three rainfall events are obtained, as shown in fig. 9(a) to 9 (c). Although the average rainfall intensity of the third rainfall event is greater than that of the first rainfall event and that of the second rainfall event, the rainfall duration is short, so that the early warning result shows that most areas are subjected to three-level early warning, and only a small number of grid units are subjected to two-level early warning, so that the whole system is safe. The first early warning occurs in both the first rainfall event and the second rainfall event, but most of the area of the first early warning is located in the ant mountain section (the black-line frame area in fig. 9), which indicates that the landslide of the section is likely to occur, and important attention needs to be paid. Most rainfall-induced landslides in the research area also occur in the ant mountain section, which proves the reasonability of the early warning result of the application.
In summary, the method takes the general construction high speed of Yunnan province as a research object, rainfall threshold estimation and meteorological early warning as targets, utilizes a numerical model TRIGRS (Transient rain infiltration and grid based regional slope-stability model) to simulate the change of Transient pore water pressure of each grid unit in the research area of the research object in the rainfall infiltration process, and calculates the stability of each grid unit; secondly, establishing a mathematical function relation between the average rainfall intensity (I) and the rainfall duration (D) and the stability (stable or unstable) of each evaluation unit by using a TRIGRS model by taking the grid unit as the evaluation unit so as to reduce the calculation cost for searching the rainfall I-D threshold value and improve the precision of rainfall threshold value solving; then, solving a rainfall I-D threshold value of each grid unit in the research area to obtain the spatial distribution of the rainfall I-D threshold values; and finally, establishing a three-level meteorological early warning scheme of the general high-speed research area by combining real-time rainfall data on the basis of the rainfall threshold spatial distribution map. And three rainfall events of 8 months in 2020 are used for explaining how to use the rainfall threshold value to carry out landslide early warning, and the early warning level is given. And results show that the primary early warning area is basically consistent with the historical landslide frequent occurrence area, and the reasonability of the early warning result is proved. The method and the system provide important guarantee for building high-speed safe operation and provide reference for building a rainfall threshold-based meteorological early warning system for similar linear projects.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be further noted that, in the present application, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. "and/or" means that either or both of them can be selected. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The mountain highway rainfall induced landslide zoning classification early warning method along the line is introduced in detail, a specific example is applied in the method to explain the principle and the implementation mode of the method, and the description of the embodiment is only used for helping to understand the method and the core idea of the method; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (8)
1. A rainfall-induced landslide partitioned early warning method along a mountain expressway is characterized by comprising the following steps:
step S1: determining a research area where rainfall along a highway is easy to induce landslide, generating a plurality of training samples in the sampling range of the average rainfall intensity and the rainfall duration of the research area, and inputting each training sample into a TRIGRS model, wherein the TRIGRS model is used for simulating the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process so as to output and obtain a stability coefficient of each grid unit;
step S2: training each training sample and the stability coefficient of each grid unit corresponding to the training sample by using an LSSVM (least squares support vector machine), and establishing a stability discrimination model of each grid unit; the stability discrimination model is used for representing a mathematical function relation between the average rainfall intensity and the rainfall duration and the stability of each grid unit;
step S3: calculating an average rainfall intensity-rainfall duration threshold value of each grid unit in the research area according to the stability discrimination model of each grid unit, and obtaining the spatial distribution of the average rainfall intensity-rainfall duration threshold value in the research area;
step S4: and establishing a meteorological early warning scheme for the rainfall-induced landslide in the research area according to the spatial distribution of the average rainfall intensity-rainfall duration threshold value in the research area and the rainfall data obtained in real time at present.
2. The sectional early warning method for rainfall-induced landslide along the mountain highway according to claim 1 wherein in step S1, the step of using the TRIGRS model to simulate the transient pore water pressure change of each grid unit in the research area during rainfall infiltration to output the stability coefficient of each grid unit comprises:
substep S1-1: simulating the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process by using a linear solving form of a Ricketts equation:
(1) where θ is a volume water content of each grid cell, δ is a gradient of each grid cell, and K (ψ) is a hydraulic transfer function of a soil body, where θ and K (ψ) are expressed as follows, respectively, in accordance with a pressure head ψ of each grid cell:
θ＝θr+(θs-θr)exp(α'ψ*) (2)，
K(ψ)＝Ksexp(α'ψ*)(3)，
(2) in the formula (3), KsIs the saturation permeability coefficient of the soil body, thetarIs the residual volume water content, θsIs the saturated volume water content, alpha' is the Gardner parameter,. phi. phi. -phi0，ψ0Is a constant equal to 0 or-1/α';
substep S1-2: according to the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process, the TRIGRS model calculates the stability coefficient FS of each grid unit by using one-dimensional infinite slope stability:
wherein the content of the first and second substances,
(4) wherein c' is the effective cohesive force of the soil body,
is the effective internal friction angle, gamma, of the soil masswIs waterOf gravity,. gammasIs the gravity of the soil body; in the TRIGRS model, the pressure head ψ is a function of the groundwater level depth Z and time t of the investigation region;
wherein FS >1 represents that the slope corresponding to the grid cell is stable, FS ═ 1 represents that the slope corresponding to the grid cell is in a limit equilibrium state, and FS <1 represents that the slope corresponding to the grid cell is unstable.
3. The mountain highway rainfall induced landslide zoned grading pre-warning method according to claim 2, wherein in the TRIGRS model, the groundwater level depth Z is expressed as a constant percentage of the study zone soil thickness; the method further comprises the following steps:
determining the soil thickness d of each grid cell in the study area using a soil thickness versus slope relationshipsWherein:
in the formula, zmaxAnd zminMaximum and minimum values of the thickness of the soil in the investigation region, respectively, delta being the slope of each grid cell, deltamaxAnd deltaminThe maximum and minimum values of the gradient of the study area are respectively.
4. The mountain highway rainfall-induced landslide zoning graded early warning method according to claim 2, wherein the step S3 comprises:
substep S3-1: aiming at the stability discrimination model of each grid unit, Matlab is utilized to fix the rainfall duration D, the average rainfall intensity I is increased from small to large, and the stability discrimination model is used for the first occurrence of FS<1 is the critical condition of the grid unit, the average rainfall intensity corresponding to the grid unit reaching the critical condition is obtained, and the average rainfall intensity corresponding to the grid unit reaching the critical condition is taken as the critical drop under the rainfall duration DRain intensity Ic；
Substep S3-2: changing the rainfall duration D, solving the critical rainfall intensity of the grid unit under different rainfall durations to obtain Ic-a D data set;
substep S3-3: according to the formula IcA data set D, solving the slope and the intercept of the grid unit in a rainfall threshold model by using least square regression, and obtaining a rainfall I-D threshold curve of the grid unit according to the slope and the intercept;
substep S3-4: and traversing each grid unit, and drawing a spatial distribution diagram of the rainfall I-D threshold of the research area according to the rainfall I-D threshold curve of each grid unit.
5. The mountain highway rainfall induced landslide zone grading early warning method according to claim 4, wherein the rainfall threshold model is as follows:
Ic＝αDβ (5)，
(5) in the formula IcIs the critical average rainfall intensity of the power law equation in mm/h; d is the duration of rainfall in units of h; α is a scale parameter and β is a shape parameter related to the slope of the power-law curve;
by using the base 10 logarithm for equation (5), the equation is expressed as:
logIc＝βlogD+logα (6)，
in which beta is logIc-the slope of the logD line, representing the slope of the grid cell in the rainfall threshold model, and Log α being the intersection with the ordinate axis, representing the intercept of the grid cell in the rainfall threshold model.
6. The method of claim 5, wherein the rainfall-induced landslide zonal grading pre-warning method along the mountain highway is characterized in that in the rainfall threshold model, the upper bound D of the rainfall duration D ismaxAt 200h, duration of rainfall DminTo search for the minimum duration of rainfall for the critical rainfall intensity.
7. The mountain highway rainfall-induced landslide zone grading pre-warning method according to claim 1, further comprising:
randomly generating a plurality of test samples by using Latin hypercube sampling;
respectively using the TRIGIS model and a stability discrimination model obtained by training to calculate the stability corresponding to each test sample;
and traversing all the test samples, comparing the stability calculation results of the TRIGIS model and the stability discrimination model on the same test sample, and calculating the accuracy of the stability discrimination model.
8. The mountain highway rainfall-induced landslide zoned grading pre-warning method according to claim 4, wherein the research area rainfall-induced landslide weather pre-warning scheme established in the step S4 is of three levels, comprising:
if the average rainfall intensity of a certain rainfall event in any grid unit of the research area reaches the critical rainfall intensity of the grid unit, determining that the early warning level is first grade; wherein, under one-level early warning level, the meteorological early warning scheme includes: immediately issuing early warning to remind passing vehicles, immediately organizing workers to patrol sections corresponding to the first-level early warning level, immediately closing the expressway if dangerous cases are found, and recovering operation after the dangerous cases are eliminated;
if the average rainfall intensity of a certain rainfall event in any grid unit of the research area reaches 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is the second level; wherein, under the second grade early warning level, the meteorological early warning scheme includes: issuing early warning to remind passing vehicles and pay close attention to the change of rainfall intensity, and immediately updating and issuing the early warning level when the rainfall intensity reaches the first-level early warning level;
if the average rainfall intensity of a rainfall event in any grid unit of the research area is less than 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is three levels; wherein, under tertiary early warning level, the meteorological early warning scheme includes: and not issuing early warning, but paying attention to the change of rainfall intensity in real time and updating the early warning level in real time.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114994804A (en) * | 2022-05-26 | 2022-09-02 | 郑州大学 | Urban rainfall waterlogging grading early warning method |
| CN117114415A (en) * | 2023-09-25 | 2023-11-24 | 河海大学 | Landslide hazard rapid distinguishing method for evaluating rainfall on shallow landslide induction probability |
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103558360A (en) * | 2013-11-11 | 2014-02-05 | 青岛理工大学 | Method for determining critical instable starting rainfall capacity of rainfall type landslip |
| CN104318103A (en) * | 2014-10-23 | 2015-01-28 | 中国科学院、水利部成都山地灾害与环境研究所 | Landslide hazard monitoring and early warning rainfall threshold judging method |
| KR20150128217A (en) * | 2014-05-09 | 2015-11-18 | 세종대학교산학협력단 | Method for analyzing landslide susceptibility and record media recorded program for implement thereof |
| CN105788180A (en) * | 2016-04-14 | 2016-07-20 | 中国科学院、水利部成都山地灾害与环境研究所 | Shallow rainfall-induced landslide early-warning method based on unsaturated soil mass water content |
| KR101788104B1 (en) * | 2016-06-03 | 2017-11-15 | 한국과학기술원 | System and method for real-time early warning for extreme rainfall-induced landslides |
| WO2018119880A1 (en) * | 2016-12-29 | 2018-07-05 | 柳成荫 | Early warning method and device for rainfall induced landslides based on rainfall and soil moisture |
| CN108776851A (en) * | 2018-06-11 | 2018-11-09 | 广州地理研究所 | A kind of shallow failure disaster alarm Threshold that heavy rain induces |
| CN110703360A (en) * | 2019-10-16 | 2020-01-17 | 西北大学 | Three-dimensional effect model for landslide prediction based on rainfall intensity and threshold value |
| CN111563621A (en) * | 2020-04-30 | 2020-08-21 | 中国地质调查局武汉地质调查中心 | Method, system, device and storage medium for assessing risk of regional landslide |
| CN111563619A (en) * | 2020-04-28 | 2020-08-21 | 杭州湖玛科技有限公司 | Rainfall threshold analysis method for causing watershed landslide risk |
| CN111625954A (en) * | 2020-05-22 | 2020-09-04 | 中国地质大学（武汉） | Parallel optimization method and system for rainfall type landslide model TRIGRS |
| CN111681393A (en) * | 2020-06-03 | 2020-09-18 | 福州大学 | TRIGRS-based slag disposal site instability early warning method and system |
| CN112415582A (en) * | 2020-10-15 | 2021-02-26 | 山东大学 | Three-dimensional speed geological modeling method for random arrangement of structure and wave velocity |
| KR20210062359A (en) * | 2019-11-21 | 2021-05-31 | 대한민국(산림청 국립산림과학원장) | Landslide Risk Forecast Device and Method using Rainfall Duration |
| CN113012399A (en) * | 2021-02-25 | 2021-06-22 | 中原工学院 | Rainfall type landslide early warning method and system |
| CN113449421A (en) * | 2021-06-29 | 2021-09-28 | 国家电网公司西南分部 | Landslide stability analysis method based on Morgenstrin-Price method |
-
2021
- 2021-12-02 CN CN202111464403.8A patent/CN114236095B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103558360A (en) * | 2013-11-11 | 2014-02-05 | 青岛理工大学 | Method for determining critical instable starting rainfall capacity of rainfall type landslip |
| KR20150128217A (en) * | 2014-05-09 | 2015-11-18 | 세종대학교산학협력단 | Method for analyzing landslide susceptibility and record media recorded program for implement thereof |
| CN104318103A (en) * | 2014-10-23 | 2015-01-28 | 中国科学院、水利部成都山地灾害与环境研究所 | Landslide hazard monitoring and early warning rainfall threshold judging method |
| CN105788180A (en) * | 2016-04-14 | 2016-07-20 | 中国科学院、水利部成都山地灾害与环境研究所 | Shallow rainfall-induced landslide early-warning method based on unsaturated soil mass water content |
| KR101788104B1 (en) * | 2016-06-03 | 2017-11-15 | 한국과학기술원 | System and method for real-time early warning for extreme rainfall-induced landslides |
| WO2018119880A1 (en) * | 2016-12-29 | 2018-07-05 | 柳成荫 | Early warning method and device for rainfall induced landslides based on rainfall and soil moisture |
| CN108776851A (en) * | 2018-06-11 | 2018-11-09 | 广州地理研究所 | A kind of shallow failure disaster alarm Threshold that heavy rain induces |
| CN110703360A (en) * | 2019-10-16 | 2020-01-17 | 西北大学 | Three-dimensional effect model for landslide prediction based on rainfall intensity and threshold value |
| KR20210062359A (en) * | 2019-11-21 | 2021-05-31 | 대한민국(산림청 국립산림과학원장) | Landslide Risk Forecast Device and Method using Rainfall Duration |
| CN111563619A (en) * | 2020-04-28 | 2020-08-21 | 杭州湖玛科技有限公司 | Rainfall threshold analysis method for causing watershed landslide risk |
| CN111563621A (en) * | 2020-04-30 | 2020-08-21 | 中国地质调查局武汉地质调查中心 | Method, system, device and storage medium for assessing risk of regional landslide |
| CN111625954A (en) * | 2020-05-22 | 2020-09-04 | 中国地质大学（武汉） | Parallel optimization method and system for rainfall type landslide model TRIGRS |
| CN111681393A (en) * | 2020-06-03 | 2020-09-18 | 福州大学 | TRIGRS-based slag disposal site instability early warning method and system |
| CN112415582A (en) * | 2020-10-15 | 2021-02-26 | 山东大学 | Three-dimensional speed geological modeling method for random arrangement of structure and wave velocity |
| CN113012399A (en) * | 2021-02-25 | 2021-06-22 | 中原工学院 | Rainfall type landslide early warning method and system |
| CN113449421A (en) * | 2021-06-29 | 2021-09-28 | 国家电网公司西南分部 | Landslide stability analysis method based on Morgenstrin-Price method |
Non-Patent Citations (4)
| Title |
|---|
| BAUM R L, SAVAGE W Z, GODT J W: "a Fortran program for transient rainfall infiltration and grid-based regional slope-stability analysis", 《COLORADO:US DEPARTMENT OF THE INTERIOR AND US GEOLOGICAL SURVEY》, vol. 31, no. 6, pages 168 - 179 * |
| 刘磊;殷坤龙;王佳佳;张俊;黄发明;: "降雨影响下的区域滑坡危险性动态评价研究――以三峡库区万州主城区为例", 岩石力学与工程学报, vol. 35, no. 03, pages 558 - 568 * |
| 姜玉杰, 席利飞, 董营营等: "云南省通建高速公路沿线滑坡降雨阈值估计", 《公路》, vol. 68, no. 03, pages 262 - 272 * |
| 徐嵩基;王育奎;: "公路软岩隧道底鼓注浆加固厚度数值模拟", 山东交通学院学报, no. 03, 15 September 2016 (2016-09-15), pages 60 - 66 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114994804A (en) * | 2022-05-26 | 2022-09-02 | 郑州大学 | Urban rainfall waterlogging grading early warning method |
| CN114994804B (en) * | 2022-05-26 | 2023-08-22 | 郑州大学 | Urban rainfall waterlogging grading early warning method |
| CN117114415A (en) * | 2023-09-25 | 2023-11-24 | 河海大学 | Landslide hazard rapid distinguishing method for evaluating rainfall on shallow landslide induction probability |
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| CN117291432A (en) * | 2023-11-27 | 2023-12-26 | 安徽金海迪尔信息技术有限责任公司 | Water resource scheduling monitoring system based on artificial intelligence |
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