CN114236095B - Regional grading early warning method for rainfall induced landslide along mountain expressway - Google Patents
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Abstract
The application discloses a regional and hierarchical early warning method for rainfall-induced landslide along a highway in a mountain area, which relates to the technical field of landslide disaster early warning, and the method utilizes a TRIGRS model to simulate the change of transient pore water pressure of each grid unit in a research area along the highway where rainfall is easy to induce landslide in a rainfall infiltration process, and calculates the stability of each grid unit; secondly, using a grid unit as an evaluation unit, and 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 so as to reduce the calculation cost of searching a rainfall I-D threshold value and improve the precision of solving the rainfall threshold value; 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 value; and finally, based on a rainfall threshold space distribution diagram and in combination with real-time rainfall data, a three-level weather early warning scheme of a research area is established, and a guarantee is provided for the safe operation of the expressway.
Description
Technical Field
The application relates to the technical field of landslide hazard early warning, in particular to a regional and hierarchical early warning method for rainfall induction landslide along a mountain expressway.
Background
Landslide is a common and widespread natural phenomenon, but due to the lack of an effective early warning system, a large number of casualties and huge property losses are caused worldwide. Among the many triggers, rainfall is the most dominant factor in inducing landslide. Therefore, establishing a weather early warning scheme for a study area using a rainfall threshold (defined as a rainfall condition when landslides may be induced to be reached or exceeded) is one of the most common methods. In order to ensure the safety of important linear engineering such as expressways and the like during operation, the development of rainfall threshold estimation and weather early warning research of rainfall induced landslide along the highway has important theoretical and practical significance.
The highway isoline engineering has the characteristic of extension, and the topography, geological conditions, rock-soil body characteristics and hydrologic conditions along the line have certain differences; in addition, because the linear engineering such as expressways is mainly threatened by geological disasters on two sides of the line, only the areas in a certain range (such as 500-1000 m) on two sides of the line need to be evaluated, and therefore the research area has the characteristic of a small area in the direction perpendicular to the line. Based on the characteristics of the research area, the traditional experience method is used for obtaining a regional threshold value, so that the research on the estimation of the rainfall threshold value and the early warning of the meteorological phenomenon of the expressway is carried out at present, and the attempt on the aspect is lacking.
Disclosure of Invention
The embodiment of the application provides a regional grading early warning method for rainfall-induced landslide along a mountain highway, which aims at carrying out rainfall threshold estimation and meteorological early warning on the rainfall-induced landslide along the highway and provides guarantee for safe operation of the highway.
In order to solve the problems, the embodiment of the application discloses a regional and hierarchical early warning method for rainfall induced landslide along a mountain highway, which comprises the following steps:
step S1: determining a research area of a highway along which rainfall is easy to induce landslide, generating a plurality of training samples in a sampling range of average rainfall intensity and 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 a 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, and establishing a stability judging model of each grid unit; the stability judging model is used for representing a mathematical function relation between average rainfall intensity and rainfall duration and stability of each grid unit;
Step S3: calculating the average rainfall intensity-rainfall duration threshold value of each grid unit of the research area according to the stability judging model of each grid unit, and obtaining the spatial distribution of the average rainfall intensity-rainfall duration threshold value of the research area;
step S4: and establishing a weather early warning scheme of the rainfall-induced landslide of the research area according to the spatial distribution of the average rainfall intensity-rainfall duration threshold value of the research area and the rainfall data obtained in real time currently.
Further, in the 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 the rainfall infiltration process to output and obtain the stability coefficient of each grid unit includes:
substep S1-1: using a linear solution form of a rational equation to simulate the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process:
(1) Where θ is the volumetric water content of each grid cell, δ is the slope of each grid cell, and K (ψ) is the hydraulic transfer function of the soil mass, where θ and K (ψ) are expressed as the following equation, respectively, in terms of the pressure head ψ of each grid cell:
θ＝θ r +(θ s -θ r )exp(α'ψ * ) (2)，
K(ψ)＝K s exp(α'ψ * ) (3)，
(2) In the formula (3), K s Is the saturation permeability coefficient of soil body, theta r Is the residual volume moisture content, θ s Is the saturation volume moisture content, α' is the Gardner parameter, ψ=ψ - ψ 0 ，ψ 0 Is 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 rainfall infiltration process in the research area, the TRIGRS model calculates the stability coefficient FS of each grid unit by using one-dimensional infinite slope stability:
wherein,
(4) Wherein c' is the effective cohesion of the soil mass,is the effective internal friction angle of soil body, gamma w Is the gravity of water, gamma s Is the weight of 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 unit is stable, fs=1 represents that the slope corresponding to the grid unit is in a limit balance state, and FS <1 represents that the slope corresponding to the grid unit is unstable.
Further, in the TRIGRS model, the groundwater level depth Z is expressed as a constant percentage of the soil thickness of the investigation region; the method further comprises the steps of:
determining the soil thickness d of each grid cell in the investigation region using the soil thickness versus slope s Wherein:
wherein z is max And z min Respectively the maximum value and the minimum value of the soil thickness of the research area, wherein delta is the gradient of each grid unit, delta max And delta min Maximum and minimum values of the slope of the investigation region, respectively.
Further, the step S3 includes:
substep S3-1: for the stability judging model of each grid unit, fixing the rainfall duration D by Matlab, continuously increasing the average rainfall intensity I from small to large, and using the stability judging model to appear FS for the first time<1 is a critical condition of the grid unit, 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 critical rainfall intensity I under the rainfall duration D c ；
Substep S3-2: changing rainfall duration D, solving critical rainfall intensity of the grid units under different rainfall durations to obtain I c -D data sets;
substep S3-3: according to the I c -D data set, solving a slope and an intercept of the grid unit in a rainfall threshold model using least squares regression, and obtaining a rainfall I-D threshold curve of the grid unit from the slope and the intercept;
Substep S3-4: 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, the rainfall threshold model is:
I c ＝αD β (5)，
(5) Wherein I is c The critical average rainfall intensity of the power law equation is given in mm/h; d is rainfall duration, unit h; alpha is a scale parameter and beta is a shape parameter related to the slope of the power law curve;
by using a base 10 logarithm for equation (5), this equation is expressed as:
logI c ＝βlogD+logα (6)，
wherein beta is logI c -slope of Log d line representing slope of the grid unit in the rainfall threshold model, log a being the intersection with the ordinate axis representing intercept of the grid unit in the rainfall threshold model.
Further, in the rainfall threshold model, an upper bound D of rainfall duration D max D of rainfall duration D of 200h min To find the minimum rainfall duration of critical rainfall intensity.
Further, the method further comprises:
randomly generating a plurality of test samples using Latin hypercube sampling;
calculating the stability corresponding to each test sample by using the TRIGIS model and the stability judging model obtained through training;
Traversing all the test samples, comparing the stability calculation results of the same test sample by the TRIGIS model and the stability judgment model, and calculating the accuracy of the stability judgment model.
Further, the weather early warning scheme of the rainfall-induced landslide of the research area established in the step S4 is three-stage, and includes:
if the average rainfall intensity of a certain rainfall event in any grid unit in the research area reaches the critical rainfall intensity of the grid unit, determining the early warning level as a first level; under the first-level early warning level, the meteorological early warning scheme comprises: immediately issuing early warning to remind the passing vehicles, immediately organizing staff to patrol the section corresponding to the first-level early warning level, and immediately closing the expressway if dangerous situations are found, and recovering operation after the dangerous situations are eliminated;
if the average rainfall intensity of a certain rainfall event in any grid unit in the research area reaches 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is a second level; under the second-level early warning level, the meteorological early warning scheme comprises: releasing early warning to remind passing vehicles and pay close attention to the change of rainfall intensity, and when the rainfall intensity reaches the first-level early warning level, immediately updating the early warning level and releasing the early warning level;
If the average rainfall intensity of a certain rainfall event in any grid unit in the research area is less than 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is three-level; under the tertiary early warning level, meteorological early warning scheme includes: the early warning is not issued, but the change of rainfall intensity is paid attention to in real time, and the early warning level is updated in real time.
Embodiments of the present application include the following advantages:
in the method, firstly, a research area of a slope which is easy to induce by rainfall along a highway is determined, rainfall threshold estimation and meteorological early warning are used 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, using a grid unit as an evaluation unit, and establishing a mathematical function relation between the average rainfall intensity I and the rainfall duration D and the stability (stability or instability) of each evaluation unit by using a TRIGRS model so as to reduce the calculation cost of searching a rainfall I-D threshold value and improve the precision of solving the rainfall threshold value; 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 value; and finally, based on a rainfall threshold space distribution diagram, combining real-time rainfall data to establish a three-level weather early warning scheme in the research area. Therefore, the method and the system realize rainfall threshold estimation and weather early warning of the rainfall-induced landslide along the highway, prove through relevant examples that the weather early warning has higher rationality, provide guarantee for safe operation of the highway, and provide reference for building a weather early warning system based on the rainfall threshold for similar linear engineering.
Drawings
FIG. 1 is a flow chart of steps of a regional and hierarchical early warning method for rainfall-induced landslide along a mountain highway;
FIG. 2 is a flowchart of an implementation of calculating a rainfall I-D threshold in an embodiment of the present application;
FIG. 3 is a schematic view of the location and extent of an investigation region in an embodiment of the present application;
FIGS. 4 (a) - (e) are schematic diagrams illustrating control parameters of TRIGRS model of an example research area of the present application;
fig. 5 (a) - (C) are respectively stability discrimination models of an example point a, a point B and a point C of the present application, and fig. 5 (d) is a schematic diagram of accuracy of the stability discrimination models of the example point a, the point B and the point C of the present application;
FIGS. 6 (a) -6 (c) are respectively alpha, beta and D of a rainfall threshold model min Schematic diagram of the impact on the early warning level;
FIGS. 7 (a) - (d) show I at points A, B and C, respectively c -D data points and fitted rainfall I-D threshold model curve schematic;
FIGS. 8 (a) - (d) show spatial distribution diagrams of critical rainfall intensities at rainfall durations of 12h, 24h, 48h and 72h, respectively;
fig. 9 (a) - (c) are schematic diagrams of the regional early warning results of three rainfall events, respectively.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Aiming at the technical problems of the application, the application provides a regional grading early warning method for rainfall-induced landslide along a mountain highway through the following embodiments, which aims at carrying out rainfall threshold estimation and meteorological early warning of the rainfall-induced landslide along the highway and provides guarantee for safe operation of the highway.
Referring to fig. 1, a step flow chart of a regional and hierarchical early warning method for rainfall induced landslide along a mountain highway is shown, and the method may include the following steps:
step S1: determining a research area of a highway along which rainfall is easy to induce landslide, generating a plurality of training samples in a sampling range of average rainfall intensity and rainfall duration of the research area, and inputting each training sample into a TRIGRS (Transient rainfall infiltration and grid based regional slope-stability model, numerical 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 a rainfall infiltration process so as to output and obtain a stability coefficient of each grid unit;
the research area is a part along the expressway, and a part of the expressway along which rainfall is easy to induce landslide can be selected as the research area. The research area also has the characteristic of extension, and the topography, the geological conditions, the rock-soil body characteristics and the hydrologic conditions all have certain differences along the line.
Wherein the TRIGRS model is a numerical model developed by Fortran language for regional slope stability assessment, which is capable of simulating instantaneous pore pressure changes due to rainfall infiltration and calculating the stability coefficient of each grid cell after pore water pressure changes. The model can be combined with a Geographic Information System (GIS), so that preparation of model input data and visual display of results are facilitated.
In this application, a plurality of training samples (typically 500 to 1000 samples) can be generated using uniform sampling over a sampling range of average rainfall intensity and rainfall duration of the study area, and then each training sample is input into the TRIGRS model, and the 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 each training sample, and further can output and obtain the stability coefficient of each grid unit, and the specific steps can include:
substep S1-1: using a linear solution form of a rational equation to simulate the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process:
(1) Where θ is the volumetric water content of each grid cell, δ is the slope of each grid cell, and K (ψ) is the hydraulic transfer function of the soil mass, where θ and K (ψ) are expressed as the following equation, respectively, in terms of the pressure head ψ of each grid cell:
θ＝θ r +(θ s -θ r )exp(α'ψ * ) (2)，
K(ψ)＝K s exp(α'ψ * ) (3)，
(2) In the formula (3), K s Is the saturation permeability coefficient of soil body, theta r Is the residual volume moisture content, θ s Is the saturation volume moisture content, α' is the Gardner parameter, ψ=ψ - ψ 0 ，ψ 0 Is 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 rainfall infiltration process in the research area, the TRIGRS model calculates the stability coefficient FS of each grid unit by using one-dimensional infinite slope stability:
wherein,
(4) Wherein c' is the effective cohesion of the soil mass,is the effective internal friction angle of soil body, gamma w Is the gravity of water, gamma s Is the weight of 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 unit is stable, fs=1 represents that the slope corresponding to the grid unit is in a limit balance state, and FS <1 represents that the slope corresponding to the grid unit is unstable.
In an embodiment of the present application, in the TRIGRS model, the groundwater level depth Z is expressed as a constant percentage of the soil thickness of the investigation region; that is, the relation between the soil thickness and the gradient can be used for determining the soil thickness d of each grid unit in the research area s Wherein:
wherein z is max And z min Respectively the maximum value and the minimum value of the soil thickness of the research area, wherein delta is the gradient of each grid unit, delta max And delta min Maximum and minimum values of the slope of the investigation region, respectively.
The remaining parameters may be determined by measuring or referencing existing documents 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, and establishing a stability judging model of each grid unit; the stability judging model is used for representing a mathematical function relation between average rainfall intensity and rainfall duration and stability of each grid unit;
the LSSVM (Least Square support vector machine) belongs to one method in the prior art, and the LSSVM algorithm can be realized by calling an LS-SVMlab (V1.8) toolbox developed by Suykens and the like.
Step S3: calculating the average rainfall intensity-rainfall duration threshold value of each grid unit of the research area according to the stability judging model of each grid unit, and obtaining the spatial distribution of the average rainfall intensity-rainfall duration threshold value of the research area;
the most widely used rainfall threshold model at present is the rainfall I-D threshold model, wherein the rainfall I-D threshold model is:
I c ＝αD β (5)，
(5) Wherein I is c The critical average rainfall intensity of the power law equation is given in mm/h; d is rainfall duration, unit h; alpha is a scale parameter and beta is a shape parameter related to the slope of the power law curve;
by using a base 10 logarithm for equation (5), this equation is expressed as:
logI c ＝βlogD+logα (6)，
wherein beta is logI c Slope of Log d line, log α is the intersection with the ordinate axis.
It should be noted that if the rainfall threshold is solved by using the conventional method, it is very inefficient to manually continuously adjust parameters of the TRIGRS model to find the rainfall threshold (rainfall I-D threshold). In addition, for different rainfall durations, the TRIGRS model needs to be continuously run to find critical rainfall intensity when reaching critical conditions, and the calculation amount is very large. In some studies, to reduce the amount of computation, in solving critical rainfall intensities of different rainfall durations, it is common to increase the increase in rainfall duration or increase the increase in average rainfall intensity, both of which reduce the resolution of the rainfall threshold. Therefore, a Least Squares Support Vector Machine (LSSVM) is introduced to construct a stability judging model, and the automatic and efficient solution of the rainfall threshold value of each grid unit is realized based on Matlab programming, and the method comprises the following detailed steps:
(1) An interface program between Matlab and TRIGRS models is written, and automatic interaction of data between the Matlab and the TRIGRS models is realized: and automatically inputting the set average rainfall intensity I and rainfall duration D value into an input file of the TRIGRS model by using Matlab, automatically operating the TRIGRS model to calculate to obtain the stability coefficient of each grid unit of the research area, and automatically reading the stability coefficient of each grid unit into the Matlab to facilitate the rainfall threshold calculation of the steps S2-S3.
(2) The stability discriminant model of each grid cell is built using the LSSVM so that the stability evaluation of the grid cell can be performed based on the computationally efficient LSSVM discriminant model instead of the computationally intensive numerical model. In the method, each training sample is brought into a TRIGRS model through the step S1, a corresponding stability coefficient can be calculated, and if the stability coefficient of a grid unit is greater 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, denoted as '0'. In step S2, model training is performed using the LSSVM based on each input (training sample) and each output (the two classification stability discrimination results ('1' and '0') corresponding to the training sample) as a training set, and a mathematical function relationship between the average rainfall intensity and the rainfall duration and the stability of each grid unit can be established, so that a stability discrimination model for establishing each grid unit is obtained.
(3) After the stability judging model of each grid unit is established, the rainfall threshold can be solved based on the stability judging model. Based on the rainfall I-D threshold model, referring to FIG. 2, the implementation flow of calculating the rainfall I-D threshold can be realized by the following steps:
substep S3-1: for the stability judging model of each grid unit, fixing the rainfall duration D by Matlab, continuously increasing the average rainfall intensity I from small to large, and using the stability judging model to appear FS for the first time<1 is a critical condition of the grid unit, 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 critical rainfall intensity I under the rainfall duration D c ；
Substep S3-2: changing rainfall duration D, solving critical rainfall intensity of the grid units under different rainfall durations to obtain I c -D data sets;
substep S3-3: according to the I c -D data set, solving a slope and an intercept of the grid unit in a rainfall threshold model using least squares regression, and obtaining a rainfall I-D threshold curve of the grid unit from the slope and the intercept;
In the application, the slope beta of each grid unit in the rainfall threshold model and the intercept log alpha of the grid unit in the rainfall threshold model can be obtained by calculating according to formulas (5) and (6) when the slope and the intercept of each grid unit in the rainfall threshold model are solved by using least square regression. After solving the slope beta and the intercept Log alpha, converting the slope beta and the intercept Log alpha into a power law equation of formula (5), and obtaining a rainfall I-D threshold curve of the grid unit.
Substep S3-4: 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 weather early warning scheme of the rainfall-induced landslide of the research area according to the spatial distribution of the average rainfall intensity-rainfall duration threshold value of the research area and the rainfall data obtained in real time currently.
In the application, the established weather early warning scheme of the rainfall-induced landslide of the research area can be set to be three-level, and comprises the following steps:
if the average rainfall intensity of a certain rainfall event in any grid unit in the research area reaches the critical rainfall intensity of the grid unit, determining the early warning grade as a first grade, wherein the early warning color can be marked as red; under the first-level early warning level, the meteorological early warning scheme comprises: immediately issuing early warning to remind the passing vehicle, immediately organizing staff to patrol the section corresponding to the first-level early warning level, immediately closing the high speed if the dangerous situation is found, and recovering operation after the dangerous situation is eliminated;
If the average rainfall intensity of a certain rainfall event in any grid unit in the research area reaches 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is two-level, and the early warning color can be marked as yellow; under the second-level early warning level, the meteorological early warning scheme comprises: releasing early warning to remind passing vehicles and pay close attention to the change of rainfall intensity, and when the rainfall intensity reaches the first-level early warning level, immediately updating the early warning level and releasing the early warning level;
if the average rainfall intensity of a certain rainfall event in any grid unit in the research area is less than 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is three-level, and the early warning color can be marked as green; under the tertiary early warning level, meteorological early warning scheme includes: the early warning is not issued, but the change of rainfall intensity is paid attention to in real time, and the early warning level is updated in real time.
Next, the implementation process and effect of the present application will be described in detail with respect to the rainfall threshold estimation and the meteorological early warning as targets by taking the high-speed building of Yunnan province as a research object.
1. Study area overview
The general construction high-speed route is located in the general sea county and the water-building county of Yunnan province, and basically runs from north to south and is 126 km long. According to the geological disaster risk assessment specification (DZT 0286-2015) related proposal: the important line construction engineering is that the evaluation range is extended to two sides of the line by 500-1000 m. Thus, the investigation region was determined to be in the range of 1000m on both sides of the line, with a total area of 128.8km 2 As shown in fig. 3. The mountain trend is basically consistent with the direction of the construction line, and the whole line passes through the two geomorphic units of the construction degraded middle and low mountain landform and the mountain basin (the sea-going basin-Qu Xi basin), and the ground elevation is 1262 m-2086 m. The route passes through the arc-shaped construction belt from north to south, the characteristics of mountain canyons of local road sections are outstanding, the terrain height difference is large, and therefore the rock Dan Jieli cracks in the region are relatively developed, and the rock mass is broken. Qu Jiang is the main water system developing along the line, which is injected from west to east into the south-to-south-disc river and belongs to the Zhujiang river system. The study area belongs to subtropical monsoon climate, the annual average temperature is 18.5 ℃, the maximum temperature is 35.1 ℃ and the minimum temperature is 3 ℃. The average annual rainfall is 853mm, and the rainfall in rainy seasons (5-10 months) accounts for 80% of the annual rainfall. The underground water type is mainly three types of bedrock fracture water, pore water and karst water, and the buried depth of the underground water is stabilized at 0.1 m-15 m during investigation. The exposed stratum is from old to new: 1) The Chengjiang group (Za) of the Jongiang series is siltstone and sandstone; 2) The jolt system lamp shadow group (Zb) is composed of limestone, silty sandstone and sandstone; 3) Fourth series (Q) 4 ) And (3) flushing and flooding the clay, sand and residual slope lamination.
Due to rainfall influence, multiple landslide disasters occur in a research area, such as mountain landslide occurs in ant mountain road sections in a general construction high-speed water-building county environment caused by 7 th early morning of 8 th 2011 and two days of continuous falling storm, so that bidirectional roads are blocked by soil and stones, traffic is forced to be interrupted, and vehicles in the tens of thousands are blocked; on 31 days of 7 months 2017, the highway is constructed to lead to the sea to Qu Jiang ant mountain road sections, and the highway from the sea to the qu river is interrupted due to landslide caused by rainfall. Most landslide in the research area occurs in the residual slope layer covered on the upper part of the bedrock, and most of landslide is shallow. Rainfall is the main causative factor of landslide in the study area: the rainfall infiltration increases the water content of the soil body of the residual slope lamination, the increase of the water content increases the volume weight of the soil body, the shear strength of the soil body is obviously reduced under the action of water, the anti-slip force is reduced, and finally the residual slope lamination covered on the upper part of the bedrock is caused to slide. Therefore, it is necessary to develop rainfall threshold estimation and weather early warning research of general construction high-speed rainfall induced landslide, and guarantee is provided for general construction high-speed safe operation.
2. Model parameters
(1) Control parameters of a model
The study area topography data used a 12.5m Digital Elevation Model (DEM) provided by ASF (https:// search. ASF. Alaska. Edu /), as shown in fig. 4 (a). Based on the 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 soil covered on the upper part of bedrock and the parameter of the buried depth of the underground water level are also key to analyzing the stability of shallow landslide. In mountainous areas, the soil thickness has a larger correlation with the slope of the terrain, generally the more gentle valley areas are thicker, and the steeper slopes are thinner in mountain top areas. The application adopts the relation between the soil thickness and the gradient proposed by Baum and the like to infer the soil thickness d of each grid unit in the research area s The following are provided:
wherein z is max And z min Respectively the maximum value and the minimum value of the soil thickness of the research area; delta is the grade of each grid cell; delta max And delta min Maximum and minimum of the grade of the study area, respectively. Based on typical longitudinal section drilling data provided by the survey report, the maximum value z of the thickness of the covering layer is disclosed max 22.4m, with a minimum value of 0m; according to FIG. 4 (c), the zone gradient maximum delta max 55 DEG, minimum delta min Is 0 deg.. After the above parameters are determined, the soil thickness parameters of the study area can be calculated by using the formula (7), and the result is shown in fig. 4 (d). In modeling using TRIGRS, the groundwater level depth is typically estimated as a constant percentage of the soil thickness. The maximum value of the buried depth of the underground water level of the research area is 15m, and the maximum value of the soil thickness is 22.4m, if the ratio of the maximum values of the two is taken as the constant percentage, the groundwater level depth of the study area can be determined as 66.96% of the soil thickness, and the result is shown in fig. 4 (e).
(2) Mechanical and hydrological parameters
The upper soil covering body of the research area is mainly residual-slope-area gravelly soil (about 10% of gravelly content), and the effective internal friction angle of the residual-slope-area gravelly soil is determined according to the geological survey dataThe effective cohesion c' is between 35kPa and 55kPa, the unit weight gamma is between 25 and 42 DEG s 27kN/m 3 Saturated water content θ s 35% residual moisture content θ r 4.8%. Saturation permeability coefficient K s Coefficient of hydraulic diffusion D 0 And initial infiltration Rate I ZLT Is relatively more difficult and is therefore determined mainly with reference to the existing literature or specifications. According to the relevant literature, the saturation permeability coefficient of the gravel soil is generally 4.6X10 -7 m/s～1.0×10 -4 Between m/s, whereas the hydraulic diffusion coefficient is usually determined to be 100 times the saturation permeability coefficient (D 0 ＝100K s ) Soil layer initial infiltration rate I ZLT Is generally smaller than D 0 Square of (d).
The parameters are further calibrated within the empirical range of parameters in order to obtain a combination of parameters that better matches the actual physical properties. Calibration condition one: because landslide does not occur in the study area when no rainfall occurs, i.e., the entire area of the study area should be stable under the condition of no rainfall (the stability coefficient FS calculated by the TRIGRS model is greater than 1); and (3) a second calibration condition: on 31 days of 7 months in 2017, the highway is constructed to lead to sea to Qu Jiang ant mountain sections, landslide occurs due to rainfall, and a Yuxi rainfall monitoring station closest to the landslide is inquired: the study area was continuously rained for 3 days before landslide occurred, and the cumulative rainfall was 90mm, indicating that the stability FS calculated using the TRIGRS model should be less than 1 in the landslide location area and most other non-landslide areas should be greater than 1 at an average rainfall intensity of 1.25mm/h and a rainfall time of 72 h. By repeating the trial and error calibration, two calibration conditions are satisfied simultaneously, 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) Parameter of rainfall
The rainfall parameters in the TRIGRS model are the average rainfall intensity (I) and the rainfall duration (D). When calculating the rainfall I-D threshold, setting the average rainfall intensity searching range to be 0.1mm/h to 50mm/h and increasing the average rainfall intensity searching range to be 0.1mm/h; the rainfall duration searching range is set to be 1-200 h, and the increment is 2h.
3. 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 marked as '1', FS is less than 1 and is marked as '0') of a research area corresponding to each training sample by using a TRIGIS model; finally, a stability discriminant model is built for each grid cell of the study area by the 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 built, since the stability factor for 95.7% of the study area simulated using the TRIGRS model under any rainfall conditions 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) to 5 (C) show training cases of the stability discrimination model of 3 grid units (points A, B and C), and it can be found that the LSSVM has a very excellent stability classification effect for the present study area. In order to check the accuracy of the stability discrimination model, the present application also provides the following method:
randomly generating a plurality of test samples using Latin hypercube sampling;
Calculating the stability corresponding to each test sample by using the TRIGIS model and the stability judging model obtained through training;
traversing all the test samples, comparing the stability calculation results of the same test sample by the TRIGIS model and the stability judgment model, and calculating the accuracy of the stability judgment model.
100 test samples are additionally 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 through training. By comparing the results, the accuracy of the stability discrimination model can be calculated, and the result is shown in fig. 5 (d). The accuracy of the stability judging model of all grid units can reach 97% through statistics of the accuracy of the judging model, and the stability judging model of each grid unit has good accuracy. Therefore, the stability judging model can be used for completely replacing the TRGIS model to evaluate the stability of each grid unit, so that the calculation cost for searching the rainfall threshold value later is obviously reduced.
Next, a rainfall threshold is calculated for each grid cell according to the set rainfall parameters. Firstly, fixing rainfall duration, continuously increasing average rainfall intensity, and when the discrimination model is 0' for the first time, obtaining the corresponding average rainfall intensity as critical rainfall intensity of the rainfall duration; secondly, changing rainfall duration, solving critical rainfall intensity under different rainfall durations, and finally obtaining 100I for each grid unit c -D data points; finally, based on these data points, the slope β and intercept log α in each grid unit rainfall threshold model (equation 6) can be calculated using least squares regression, resulting in α and β in each grid unit rainfall threshold model, as shown in fig. 6 (a) and 6 (b). It is also notable that, since it takes a certain time for the rainfall to infiltrate into the deep soil, the short period of rainfall does not allow the rainwater to infiltrate into the soil sufficiently, but is a large partThe waste water is discharged through surface runoff, so that the stability is less affected. In other words, in the case of short-time rainfall, no matter how large the rainfall intensity is, the grid unit will not be unstable, and the critical rainfall intensity can be obtained only if the rainfall duration is enough to cause slope instability. Thus, the rainfall I-D threshold model is not applicable to any rainfall event of rainfall duration, but has a range of applicability. Specifically, in the rainfall threshold model, an upper bound D of rainfall duration D max D of rainfall duration D, optionally 200h min To find the minimum rainfall duration of critical rainfall intensity. The minimum rainfall duration (lower bound of the applicable range) for each grid cell is shown in fig. 6 (c).
FIGS. 7 (a) through 7 (C) illustrate I at points A, B and C, respectively c- D data points and fitted rainfall I-D threshold model. It can be found that the rainfall I-D threshold model fits I well c- D data points, illustrating that it is feasible to use TRIGRS model for rainfall threshold estimation. Fig. 7 (D) plots the rainfall I-D threshold curve for 36509 grid cells. It can be seen that the critical rainfall intensity of each grid cell gradually decreases as the duration of the rainfall increases, but the magnitude of the variation is 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 the slope instability can be caused by short heavy rain or long light rain. After the rainfall I-D threshold curve of each grid unit is obtained, the spatial distribution of critical rainfall intensity of any rainfall duration in the research area 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, for example, in the case of 48h rainfall duration, the minimum value of the critical rainfall intensity is 0.960mm/h, and the maximum value of the critical rainfall intensity reaches 15.079mm/h, and the differences are very large. This difference is reasonable because the gradient, soil thickness, etc. of each grid cell are different, and there must be a large difference in critical rainfall intensity. It is worth mentioning that However, the rainfall threshold based on empirical statistics only provides a single rainfall threshold for the research area, and the influence of differences of different spatial position topography conditions, soil thickness and the like 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 influence of the topography condition of a research area and the soil thickness difference on the rainfall threshold, and can also give the area most prone to landslide under different rainfall intensities.
This application describes how to use rainfall threshold for early warning, for example, 8 months in 2020. There are three major rainfall events in 8 months 2020: continuous rainfall is carried out for 72 hours from 31 days to 8 months and 2 days, the rainfall reaches 67.1mm, and the average rainfall intensity is 0.93mm/h; continuous rainfall is carried out for 48 hours from 16 days to 17 days of 8 months, the rainfall reaches 106.6mm, and the average rainfall intensity is 2.22mm/h; rainfall is carried out for 12 hours on 29 days of 8 months, the rainfall reaches 55.3mm, and the average rainfall intensity is 4.61mm/h. By comparing the rainfall intensity of the three rainfall events with the rainfall I-D threshold, and combining the three-level early warning scheme, the early warning result of the three rainfall events is obtained, as shown in fig. 9 (a) to 9 (c). Although the average rainfall intensity of the third rainfall event is larger than that of the first rainfall event and the second rainfall event, the rainfall duration is shorter, so that the early warning result shows that most areas are three-level early warning, and only a small number of grid units are two-level early warning, so that the whole system is safe. The first early warning occurs in both the first and second rainfall events, but most of the first early warning area is located on the ant mountain section (black line frame area in fig. 9), which indicates that the possibility of landslide in the section is high, and important attention is required. Most rainfall-induced landslide in the research area also occurs in ant mountain sections, which proves the reasonability of the early warning result of the application.
In summary, the application takes the general construction high speed of Yunnan province as a research object, takes rainfall threshold estimation and meteorological early warning as targets, utilizes a numerical model TRIGRS (Transient rainfall infiltration and grid based regional slope-stability model) to simulate the change of transient pore water pressure of each grid unit in a research area aiming at the research object in the rainfall infiltration process, and calculates the stability of each grid unit; secondly, using a grid unit as an evaluation unit, and establishing a mathematical function relation between average rainfall intensity (I) and rainfall duration (D) and stability (stable or unstable) of each evaluation unit by using a TRIGRS model so as to reduce the calculation cost of searching a rainfall I-D threshold value and improve the precision of solving the rainfall threshold value; 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 value; and finally, based on a rainfall threshold space distribution diagram, combining real-time rainfall data to establish a three-level weather early warning scheme of the general high-speed research area. And three rainfall events of 8 months in 2020 are used for explaining how to use rainfall threshold values for landslide early warning, and early warning grades are given. The result shows that the primary early warning area is basically consistent with the history landslide frequent area, and the rationality of the early warning result is proved. The method provides important guarantee for general construction of high-speed safe operation, and provides reference for building a weather early warning system based on rainfall threshold value for similar linear engineering.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present embodiments 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 on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, 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 apparatus 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 apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it is further noted that in this 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 either or both of which may be selected. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The above describes the method for regional and hierarchical early warning of rainfall-induced landslide along the expressway in the mountain area, and specific examples are applied to the explanation of the principle and the implementation mode of the method, and the explanation of the above examples is only used for helping to understand the method and the core idea of the method; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (7)
1. The regional grading early warning method for rainfall induced landslide along mountainous highways is characterized by comprising the following steps:
step S1: determining a research area of a highway along which rainfall is easy to induce landslide, generating a plurality of training samples in a sampling range of average rainfall intensity and 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 a 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, and establishing a stability judging model of each grid unit; the stability judging model is used for representing a mathematical function relation between average rainfall intensity and rainfall duration and stability of each grid unit;
step S3: calculating the average rainfall intensity-rainfall duration threshold value of each grid unit of the research area according to the stability judging model of each grid unit, and obtaining the spatial distribution of the average rainfall intensity-rainfall duration threshold value of the research area;
step S4: establishing a weather early warning scheme of the rainfall-induced landslide of the research area according to the spatial distribution of the average rainfall intensity-rainfall duration threshold value of the research area and the rainfall data obtained in real time currently;
wherein, the step S3 includes:
substep S3-1: for the stability judging model of each grid unit, fixing the rainfall duration D by Matlab, continuously increasing the average rainfall intensity I from small to large, taking the first occurrence of FS <1 of the stability judging model as the critical condition of the grid unit, obtaining the average rainfall intensity corresponding to the grid unit reaching the critical condition, and taking the average rainfall intensity corresponding to the grid unit reaching the critical condition as the critical rainfall intensity Ic under the rainfall duration D;
Substep S3-2: changing rainfall duration D, and solving critical rainfall intensities of the grid units under different rainfall durations to obtain an Ic-D data set;
substep S3-3: solving the slope and intercept of the grid unit in a rainfall threshold model by utilizing least square regression according to the Ic-D data set, and obtaining a rainfall I-D threshold curve of the grid unit according to the slope and the intercept;
substep S3-4: traversing each grid unit, calculating to obtain a rainfall I-D threshold curve of 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.
2. The method according to claim 1, wherein in step S1, the TRIGRS model is used for simulating the transient pore water pressure change of each grid unit in the research area during the rainfall infiltration process, so as to output and obtain the stability coefficient of each grid unit, and the method comprises the following steps:
substep S1-1: using a linear solution form of a rational equation to simulate the change of transient pore water pressure of each grid unit in the research area in the rainfall infiltration process:
(1) Where θ is the volumetric water content of each grid cell, δ is the slope of each grid cell, and K (ψ) is the hydraulic transfer function of the soil mass, where θ and K (ψ) are expressed as the following equation, respectively, in terms of the pressure head ψ of each grid cell:
θ＝θ r +(θ s -θ r )exp(α'ψ * ) (2)，
K(ψ)＝K s exp(α'ψ * ) (3)，
(2) In the formula (3), K s Is the saturation permeability coefficient of soil body, theta r Is the residual volume moisture content, θ s Is the saturation volume moisture content, α' is the Gardner parameter, ψ=ψ - ψ 0 ，ψ 0 Is 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 rainfall infiltration process in the research area, the TRIGRS model calculates the stability coefficient FS of each grid unit by using one-dimensional infinite slope stability:
wherein,
(4) Wherein c' is the effective cohesion of the soil mass,is the effective internal friction angle of soil body, gamma w Is the gravity of water, gamma s Is the weight of 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 unit is stable, fs=1 represents that the slope corresponding to the grid unit is in a limit balance state, and FS <1 represents that the slope corresponding to the grid unit is unstable.
3. The mountain highway rainfall-induced landslide zoning and grading early warning method according to claim 2, wherein in a 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 steps of:
determining a soil thickness ds of each grid cell in the investigation region using a soil thickness versus slope relationship, wherein:
wherein z is max And z min Respectively the maximum value and the minimum value of the soil thickness of the research area, wherein delta is the gradient of each grid unit, delta max And delta min Maximum and minimum values of the slope of the investigation region, respectively.
4. The mountain highway rainfall-induced landslide zoning and grading early warning method according to claim 1, wherein the rainfall threshold model is as follows:
I c ＝αD β (5)，
(5) Wherein I is c The critical average rainfall intensity of the power law equation is given in mm/h; d is rainfall duration, unit h; alpha is a scale parameter and beta is a shape parameter related to the slope of the power law curve;
by using a base 10 logarithm for equation (5), this equation is expressed as:
logI c ＝βlogD+logα (6)，
wherein beta is logI c -slope of Log d line representing slope of the grid unit in the rainfall threshold model, log a being the intersection with the ordinate axis representing intercept of the grid unit in the rainfall threshold model.
5. The method for regional and hierarchical early warning of rainfall induced landslide along mountain highways of claim 4, wherein, in said mountain highwayIn the rainfall threshold model, the upper bound D of rainfall duration D max D of rainfall duration D of 200h min To find the minimum rainfall duration of critical rainfall intensity.
6. The mountain highway rainfall-induced landslide zoning and grading early warning method according to claim 1, further comprising:
randomly generating a plurality of test samples using Latin hypercube sampling;
calculating the stability corresponding to each test sample by using the TRIGIS model and the stability judging model obtained through training;
traversing all the test samples, comparing the stability calculation results of the same test sample by the TRIGIS model and the stability judgment model, and calculating the accuracy of the stability judgment model.
7. The regional and hierarchical early warning method for rainfall-induced landslide along mountain highways according to claim 1, wherein the meteorological early warning scheme for the rainfall-induced landslide of the research area established in step S4 is three-stage, and comprises:
if the average rainfall intensity of a certain rainfall event in any grid unit in the research area reaches the critical rainfall intensity of the grid unit, determining the early warning level as a first level; under the first-level early warning level, the meteorological early warning scheme comprises: immediately issuing early warning to remind the passing vehicles, immediately organizing staff to patrol the section corresponding to the first-level early warning level, and immediately closing the expressway if dangerous situations are found, and recovering operation after the dangerous situations are eliminated;
If the average rainfall intensity of a certain rainfall event in any grid unit in the research area reaches 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is a second level; under the second-level early warning level, the meteorological early warning scheme comprises: releasing early warning to remind passing vehicles and pay close attention to the change of rainfall intensity, and when the rainfall intensity reaches the first-level early warning level, immediately updating the early warning level and releasing the early warning level;
if the average rainfall intensity of a certain rainfall event in any grid unit in the research area is less than 50% of the critical rainfall intensity of the grid unit, determining that the early warning level is three-level; under the tertiary early warning level, meteorological early warning scheme includes: the early warning is not issued, but the change of rainfall intensity is paid attention to in real time, and the early warning level is updated in real time.
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