CN113139020A - Self-adaptive migration method of landslide monitoring and early warning model - Google Patents
Self-adaptive migration method of landslide monitoring and early warning model Download PDFInfo
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Abstract
The invention discloses a self-adaptive migration method of a landslide monitoring and early warning model, which comprises the steps of firstly analyzing the similarity of a newly-built monitoring point and the geographic environment of an existing monitoring point in a certain spatial range to obtain the similarity of the newly-built monitoring point and the existing monitoring point; then, based on the early warning model of the existing monitoring point, combining with the similarity metric value, deducing an initial early warning model of the newly-built monitoring point; and finally, after the newly-built monitoring point operates, the initial early warning model can automatically adjust the similarity weight coefficient according to the monitoring early warning result, and the gradual optimization of the model is realized. The method has a self-adaptive process, and the model parameters can be self-adjusted along with the extension of the monitoring time and the occurrence of false alarms, so that the reliability of early warning is gradually improved. The method can transfer the early warning model of the existing monitoring point to the newly-built monitoring point, and has high practical value.
Description
Technical Field
The invention belongs to the technical field of geological monitoring, and particularly relates to a self-adaptive migration method of a landslide monitoring and early warning model.
Background
The current landslide monitoring and early warning method mainly utilizes a single index to carry out monitoring and early warning or utilizes known data to fix multi-index weight and multi-parameter combination to carry out monitoring and early warning, and only aims at a specific monitoring place, and cannot be migrated for use.
Disclosure of Invention
Aiming at the technical problems, the invention provides a self-adaptive migration method of a landslide monitoring and early warning model, which mainly considers the geographical environment correlation of the existing monitoring points and the newly-built monitoring points and the full utilization of the historical early warning results.
In order to achieve the above object, the technical scheme adopted by the invention is as follows:
a self-adaptive migration method of a landslide monitoring and early warning model comprises the following steps:
firstly, analyzing the geographic environment similarity of a newly-built monitoring point and an existing monitoring point in a certain spatial range to obtain the similarity of the newly-built monitoring point and the existing monitoring point;
then, based on the early warning model of the existing monitoring point, combining with the similarity metric value, deducing an initial early warning model of the newly-built monitoring point;
and finally, after the newly-built monitoring point operates, the initial early warning model can automatically adjust the similarity weight coefficient according to the monitoring early warning result, and the gradual optimization of the model is realized.
The basic steps are as follows:
s1, collecting regional geographic environment data;
the regional geographic environment data comprise earthquake points, fracture zones, terrain slopes and vegetation coverage, and the data range covers a newly-built monitoring point region and an existing monitoring point region;
s2, grading regional geographic environment data;
unifying a coordinate frame of regional geographic environment data, grading the data, and endowing corresponding characteristic values according to data attribute grades; the eigenvalues are calculated as shown in equation 1:
in the formula, E is a characteristic value, and n is a regional geographic environment data level;
seismic point data grading: carrying out buffer area classification on the seismic point data according to the seismic level (M), and giving a characteristic value;
and (3) fracture zone data classification: making a multistage buffer zone for the broken belt data according to the length (L, unit is kilometer) of the broken belt, and endowing a characteristic value;
grading vegetation coverage data: calculating a vegetation coverage factor (N, see formula 2) from the remote sensing image, dividing the vegetation coverage factor into five grades according to a natural breakpoint method, and sequentially giving characteristic values from low to high;
in the formula, N is a vegetation coverage factor, and NDVI is a normalized vegetation index;
grading terrain gradient data: calculating a terrain gradient factor according to a Digital Elevation Model (DEM), grading according to the relation between the landslide occurrence probability and the gradient, and giving a characteristic value;
s3, calculating the similarity of the same type of geographic environment factors;
the similarity between different levels of the same type of geographic environment factors is the reciprocal of the difference of the characteristic values, and the larger the absolute value of the similarity is, the higher the similarity of the geographic environment is;
s4, calculating the accumulated similarity of the regional geographic environment, which is shown in a formula 3:
Sall＝(SE■1)+(SF■1)+(SS■1)+(SN■ 1) equation 3
In the formula, SallCumulative similarity, S, representing regional geographic environmentallIf the value is positive, the landslide occurrence probability of the newly-built monitoring point is larger than the probability of the existing monitoring point; sallIs equal to0, indicating consistency; sallIf the value is negative, namely the similarity direction is negative, the occurrence probability of the landslide of the newly-built monitoring point is smaller than that of the existing monitoring point; sERepresenting seismic factor similarity; sFRepresenting the similarity of the fracture zone factors; sSRepresenting a terrain slope factor similarity; sNRepresenting the similarity of vegetation coverage factors; ■ denotes conditional operator, which is related to the variable before it, variable S before ■I1 or SIWhen the value is less than 0, the value of I is E, F, S, N, and ■ is minus; when S isI> 0 and SIWhen not equal to 1, ■ is plus sign;
s5, migrating the existing landslide monitoring and early warning model according to the accumulated similarity of the regional geographic environment, as shown in a formula 4:
h(x)＝f(x)+k×Sallequation 4
In the formula, h (x) is a monitoring and early warning model of the newly-built monitoring point; (x) is an early warning model of the existing monitoring points; sallAccumulating similarities for regional geographic environments; k is the adaptive similarity weight coefficient when SallPositive, k is negative; otherwise, the positive is true; when the early warning model sends out false alarm, the occurrence probability of the landslide output by the model is large, the self-adaptive optimization of the model can be realized by reducing the k value, the amplitude is reduced to be the difference between the original value and the value when the false alarm occurs, and finally the reliable landslide early warning model of the newly-built monitoring point can be obtained.
The self-adaptive migration method of the landslide monitoring and early warning model has the beneficial effects that:
(1) the method has a self-adaptation process, and the model parameters can be self-adjusted along with the extension of the monitoring time and the occurrence of false alarms, so that the reliability of early warning is gradually improved.
(2) The method can transfer the early warning model of the existing monitoring point to the newly-built monitoring point, and has high practical value.
Detailed Description
The present invention will be further described with reference to the following embodiments. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
A self-adaptive migration method of a landslide monitoring and early warning model comprises the following steps:
firstly, analyzing the geographic environment similarity of a newly-built monitoring point and an existing monitoring point in a certain spatial range to obtain the similarity of the newly-built monitoring point and the existing monitoring point;
then, based on the early warning model of the existing monitoring point, combining with the similarity metric value, deducing an initial early warning model of the newly-built monitoring point;
and finally, after the newly-built monitoring point operates, the initial early warning model can automatically adjust the similarity weight coefficient according to the monitoring early warning result, and the gradual optimization of the model is realized.
The basic steps are as follows:
s1, collecting regional geographic environment data;
the regional geographic environment data comprise earthquake points, fracture zones, terrain slopes and vegetation coverage, and the data range covers a newly-built monitoring point region and an existing monitoring point region;
s2, grading regional geographic environment data;
unifying the coordinate frame of the regional geographic environment data, grading the data, and endowing corresponding characteristic values according to the data attribute grade. The eigenvalues are calculated as shown in equation 1:
in the formula, E is a characteristic value, and n is a regional geographic environment data level;
seismic point data grading: buffer zone classification is performed on seismic point data according to magnitude (M), and characteristic values are given, as detailed in Table 1:
TABLE 1 seismic point grading buffer eigenvalue (R is buffer distance, unit: kilometer)
| Rank of | Buffer staging | Characteristic value |
| Ⅰ | R≤60/(9-M) | 1 |
| Ⅱ | 60/(9-M)<R≤120/(9-M) | 3 |
| Ⅲ | 120/(9-M)<R≤180/(9-M) | 5 |
| Ⅳ | 180/(9-M)<R≤240/(9-M) | 9 |
| Ⅴ | 240/(9-M)<R | 17 |
And (3) fracture zone data classification: the data of the broken strip is buffered in multiple stages according to the length (L, unit is kilometer) of the broken strip, and characteristic values are given, which are detailed in Table 2:
TABLE 2 fractional characteristic values of the fractured zone (R is buffer distance, unit: kilometers)
Grading vegetation coverage data: calculating vegetation coverage factors (N, see formula 2) from the remote sensing images, dividing the vegetation coverage factors into five grades according to a natural breakpoint method, and sequentially giving characteristic values from low to high, which is detailed in a table 3:
in the formula, N is a vegetation coverage factor, and NDVI is a normalized vegetation index.
TABLE 3 hierarchical similarity values of overlay (Natural breakpoint method divided into 5 levels)
| Rank of | Ⅰ | Ⅱ | Ⅲ | Ⅳ | Ⅴ |
| Characteristic value | 1 | 3 | 5 | 9 | 17 |
Grading terrain gradient data: calculating a terrain gradient factor according to a Digital Elevation Model (DEM), grading according to the relation between the occurrence probability of landslide and the gradient, and giving characteristic values, which are detailed in Table 4:
TABLE 4 grading characteristic of terrain slope (S is slope, unit: degree)
| Rank of | Gradient (degree) | Probability of | Characteristic value |
| Ⅰ | 25<S≤45 | 0.6047 | 1 |
| Ⅱ | (18<S is less than or equal to 25) or (45)<S≤51) | 0.2546 | 3 |
| Ⅲ | S is less than or equal to 18 or 51<S | 0.1407 | 5 |
S3, calculating the similarity of the same type of geographic environment factors;
the similarity between different levels of the same type of geographic environment factors is the reciprocal of the difference of the characteristic values, and is shown in table 5 (taking seismic factors as an example, the positive and negative indicate the similarity direction). The larger the absolute value of the similarity is, the higher the geographic environment similarity is.
TABLE 5 similarity of seismic factors
S4, calculating the accumulated similarity of the regional geographic environment, which is shown in a formula 3:
Sall＝(SE■1)+(SF■1)+(SS■1)+(SN■ 1) equation 3
In the formula, SallCumulative similarity, S, representing regional geographic environmentallIf the value is positive, the landslide occurrence probability of the newly-built monitoring point is larger than the probability of the existing monitoring point; sallEqual to 0, indicating consistency; sallIf the value is negative (namely the similarity direction is negative), the occurrence probability of the landslide of the newly-built monitoring point is smaller than that of the existing monitoring point; sERepresenting seismic factor similarity; sFRepresenting the similarity of the fracture zone factors; sSRepresenting a terrain slope factor similarity; sNRepresenting the vegetation coverage factor similarity. ■ denotes conditional operator, which is related to the variable before it, variable S before ■I1 or SIWhen the value is less than 0 (I is E, F, S, N), ■ is minus; when S isI> 0 and SIWhen not equal to 1, ■ is a plus sign.
S5, migrating the existing landslide monitoring and early warning model according to the accumulated similarity of the regional geographic environment, as shown in a formula 4:
h(x)＝f(x)+k×Sallequation 4
In the formula, h (x) is a monitoring and early warning model of the newly-built monitoring point; (x) is an early warning model of the existing monitoring points; sallAccumulating similarities for regional geographic environments; k is the adaptive similarity weight coefficient when SallPositive, k is negative; otherwise, it is positive. When the early warning model sends out false alarm, the occurrence probability of the model output landslide is large, the self-adaptive optimization of the model can be realized by reducing the k value, and the reduction range is the original value and the false alarmAnd finally obtaining a reliable landslide early warning model of the newly-built monitoring point by the difference value between the values when the monitoring point occurs.
Claims (2)
1. A self-adaptive migration method of a landslide monitoring and early warning model is characterized by comprising the following steps:
firstly, analyzing the geographic environment similarity of a newly-built monitoring point and an existing monitoring point in a certain spatial range to obtain the similarity of the newly-built monitoring point and the existing monitoring point;
then, based on the early warning model of the existing monitoring point, combining with the similarity metric value, deducing an initial early warning model of the newly-built monitoring point;
and finally, after the newly-built monitoring point operates, the initial early warning model can automatically adjust the similarity weight coefficient according to the monitoring early warning result, and the gradual optimization of the model is realized.
2. The adaptive migration method of the landslide monitoring and early warning model according to claim 1, comprising the following specific steps:
s1, collecting regional geographic environment data;
the regional geographic environment data comprise earthquake points, fracture zones, terrain slopes and vegetation coverage, and the data range covers a newly-built monitoring point region and an existing monitoring point region;
s2, grading regional geographic environment data;
unifying a coordinate frame of regional geographic environment data, grading the data, and endowing corresponding characteristic values according to data attribute grades; the eigenvalues are calculated as shown in equation 1:
in the formula, E is a characteristic value, and n is a regional geographic environment data level;
seismic point data grading: carrying out buffer area classification on the seismic point data according to the seismic level M, and giving a characteristic value;
and (3) fracture zone data classification: making a multi-stage buffer zone for the data of the fracture zone according to the length L of the fracture zone, and giving a characteristic value; l, unit is kilometer;
and (3) vegetation coverage grading: calculating a vegetation coverage factor N from the remote sensing image, dividing the vegetation coverage factor N into five grades according to a natural breakpoint method, and sequentially giving characteristic values from low to high;
in the formula, N is a vegetation coverage factor, and NDVI is a normalized vegetation index;
grading the terrain gradient: calculating a terrain gradient factor according to the digital elevation model DEM, grading according to the relation between the landslide occurrence probability and the gradient, and giving a characteristic value;
s3, calculating the similarity of the same type of geographic environment factors;
the similarity between different levels of the same type of geographic environment factors is the reciprocal of the difference of the characteristic values, and the larger the absolute value of the similarity is, the higher the similarity of the geographic environment is;
s4, calculating the accumulated similarity of the regional geographic environment, which is shown in a formula 3:
Sall＝(SE■1)+(SF■1)+(SS■1)+(SN■ 1) equation 3
In the formula, SallCumulative similarity, S, representing regional geographic environmentallIf the value is positive, the landslide occurrence probability of the newly-built monitoring point is larger than the probability of the existing monitoring point; sallEqual to 0, indicating consistency; sallIf the value is negative, namely the similarity direction is negative, the occurrence probability of the landslide of the newly-built monitoring point is smaller than that of the existing monitoring point; sERepresenting seismic factor similarity; sFRepresenting the similarity of the fracture zone factors; sSRepresenting a terrain slope factor similarity; sNRepresenting the similarity of vegetation coverage factors; ■ denotes conditional operator, which is related to the variable before it, variable S before ■I1 or SIWhen the value is less than 0, ■ is a minus sign, and the value of I is E, F, S, N; when S isI> 0 and SIWhen not equal to 1, ■ is plus sign;
s5, migrating the existing landslide monitoring and early warning model according to the accumulated similarity of the regional geographic environment, as shown in a formula 4:
h(x)＝f(x)+k×Sallequation 4
In the formula, h (x) is a monitoring and early warning model of the newly-built monitoring point; (x) is an early warning model of the existing monitoring points; sallAccumulating similarities for regional geographic environments; k is the adaptive similarity weight coefficient when SallPositive, k is negative; otherwise, the positive is true; when the early warning model sends out false alarm, the occurrence probability of the landslide output by the model is large, the self-adaptive optimization of the model can be realized by reducing the k value, the amplitude is reduced to be the difference between the original value and the value when the false alarm occurs, and finally the reliable landslide early warning model of the newly-built monitoring point can be obtained.
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| CN114067534A (en) * | 2022-01-11 | 2022-02-18 | 山东省国土空间生态修复中心 | Geological disaster early warning method and system based on machine vision |
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