CN113111482A - Earthquake-barrier lake-flood chain disaster analysis method based on grey correlation degree - Google Patents

Earthquake-barrier lake-flood chain disaster analysis method based on grey correlation degree Download PDF

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CN113111482A
CN113111482A CN202110233753.7A CN202110233753A CN113111482A CN 113111482 A CN113111482 A CN 113111482A CN 202110233753 A CN202110233753 A CN 202110233753A CN 113111482 A CN113111482 A CN 113111482A
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雷添杰
李翔宇
田济扬
路京选
曲伟
黄锦涛
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China Institute of Water Resources and Hydropower Research
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Abstract

The invention discloses a method for analyzing earthquake-dammed lake-flood chained disasters based on grey correlation, which comprises the steps of carrying out dimensionless processing on disaster data, establishing a decision matrix, selecting a proper reference scheme, comparing an evaluation scheme with the reference scheme to obtain grey correlation coefficients between the reference scheme and each evaluation scheme, and finally obtaining the grey correlation between a primary disaster and a corresponding secondary disaster, thereby completing quantitative analysis of the correlation between chained disasters. The invention firstly provides a method for quantitatively evaluating the relevance between the primary disaster and the secondary disaster in the chain disaster by adopting a grey correlation method, and provides an accurate and effective method for researching the relevance between earthquake-dammed lake-flood chain disaster links.

Description

Earthquake-barrier lake-flood chain disaster analysis method based on grey correlation degree
Technical Field
The invention belongs to the technical field of geological disaster risk assessment, and particularly relates to a design of an earthquake-dammed lake-flood chain type disaster analysis method based on grey correlation degree.
Background
China has frequent earthquakes, and disasters caused by the earthquakes cause huge losses to cities and mountainous areas. Particularly in mountainous areas, landslide and collapse caused by earthquakes can make rocks, soil and other substances in mountains enter river channels, so that barrier lakes are formed. The potential danger of the barrier lake is very huge, and once the river breaks down the barrier lake, the induced flood threatens downstream buildings, engineering facilities and lives and properties of residents. Therefore, it is necessary to research the relevance between earthquake-barrier lake-flood chain disaster links.
In the process of chain disaster occurrence, each disaster chain is often not independent, and basically has a certain association and connection, so how to quantitatively evaluate the association between the primary disaster and the secondary disaster in the chain disaster is the key to reasonably analyze the chain disaster.
At present, the grey correlation degree is often used in the fields of risk assessment, comprehensive evaluation and the like. For example, the AHP-grey correlation degree analysis method for Jade of university of composite fertilizer industry carries out risk evaluation on hydraulic engineering in 5 aspects of economy, society, nature, management, technology and the like. The mineral separation cost is predicted by using the method based on GRS-GS BP in Yanggjust, and experimental results show that the method can accurately predict the change trend of the mineral separation cost in real time. The Lixiao of university of Others adopts a grey correlation method to establish a bridge risk evaluation model aiming at the problem of risk potential existing in the construction process of a bridge structure, and finally the problem of fuzzy accuracy of uncertain factors in the traditional risk evaluation is solved, so that the evaluation accuracy of the evaluation model is effectively improved. Based on the method, the grey correlation degree can be applied to the correlation analysis between the primary disaster and the secondary disaster in earthquake-dammed lake-flood chain disasters.
Disclosure of Invention
The invention aims to provide a gray correlation degree-based earthquake-dammed lake-flood chain disaster analysis method.
The technical scheme of the invention is as follows: the earthquake-dammed lake-flood chain disaster analysis method based on the grey correlation degree comprises the following steps:
s1, setting the earthquake, the barrier lake and the flood as 3 primary disaster nodes and setting secondary disasters derived from the earthquake, the barrier lake and the flood as secondary disaster nodes according to the earthquake-barrier lake-flood chain disasters of the analysis area.
And S2, performing attribute division on the secondary disaster node data.
And S3, constructing a decision matrix by combining attributes of each secondary disaster node data of the analysis area, and carrying out non-dimensionalization processing on the decision matrix.
S4, determining a reference data column in the decision matrix, taking an element in the reference data column as a reference scheme, and taking the rest elements in the decision matrix as an evaluation scheme.
And S5, calculating gray correlation coefficients between the reference scheme and each evaluation scheme.
And S6, calculating the grey correlation degree between the primary disaster node and each corresponding secondary disaster node according to the grey correlation coefficient between the reference scheme and each evaluation scheme.
And S7, performing prediction analysis on the earthquake-dammed lake-flood chain disasters in the analysis area according to the grey correlation degree of the primary disaster nodes and the corresponding secondary disaster nodes.
Further, the secondary disaster node data in step S2 may be specifically divided into benefit type data, cost type data, and fixed type data.
Further, the benefit type data is expressed as:
X(k)D1＝(x1(k)d1，x2(k)d1，x3(k)d1，...，xm(k)d1)
wherein X (k) D1Representing a benefit type data set, xi(k)d1After representing the dimensionalization of the quantityThe calculation formula of the ith benefit type data of (1) is:
Figure BDA0002959795940000021
wherein xi(k) Representing the ith raw data, minx, of the nondimensionalization in the benefit data seti(k) Represents the minimum value in the benefit type dataset, maxxi(k) The maximum value in the benefit type data set is represented, i is 1,2, and.
Further, the cost-type data is expressed as:
X(k)D2＝(x1(k)d2，x2(k)d2，x3(k)d2，...，xm(k)d2)
wherein X (k) D2Representing a cost-type data set, xi(k)d2Representing the ith cost type data after the dimensionalization, and the calculation formula is as follows:
Figure BDA0002959795940000022
wherein xi(k) Representing the ith raw data, minx, not dimensionalized in a cost-based dataseti(k) Represents the minimum value in the cost-based dataset, maxxi(k) The maximum value in the cost-type data set is represented, i is 1, 2., m, k is 1, 2., n, m represents the number of data in the cost-type data set, and n represents the total amount of data in the cost-type data set.
Further, the fixed-type data is expressed as:
X(k)D3＝(x1(k)d3，x2(k)d3，x3(k)d3，...，xm(k)d3)
wherein X (k) D3Representing a fixed data set, xi(k)d3Representing the ith fixed-type data after dimensionalization, the meterThe calculation formula is as follows:
Figure BDA0002959795940000031
wherein xi(k) Representing the i-th raw data, gamma, of the non-dimensionalization in a fixed dataseti(k) The fixed value in the ith secondary disaster node is represented, i 1, 2.. and m, k 1, 2.. and n represents the number of data in the permanent type data set, and n represents the total amount of data in the permanent type data set.
Further, the decision matrix constructed in step S3 is represented as:
Figure BDA0002959795940000032
wherein xi(k) Representing a m x n dimensional decision matrix xm*nThe elements in the ith row and the kth column in the middle row are 1,2,.. the m, k is 1,2,.. the n, m represents the data number of the secondary disaster node, and n represents the total data amount of the secondary disaster node.
Further, the formula for performing the non-dimensionalization processing on the decision matrix in step S3 is as follows:
Figure BDA0002959795940000033
or
Figure BDA0002959795940000034
Wherein xi(k) Representing a non-dimensionalized pre-processing decision matrix xm*nElement of ith row and kth column, xi' (k) denotes the decision matrix x after dimensionless processingm*nElement of ith row and kth column, xi(k)minRepresenting a decision matrix xm*nMinimum value element of (1), xi(k)maxRepresenting a decision matrix xm*nMaximum value element in (1).
Further, the reference data column in step S4 is represented as:
X0＝(x0(1)，x0(2)，...，x0(n))
when the ith secondary disaster node data belongs to benefit type data, x0(k)＝max{xi(k) When the ith secondary disaster node data belongs to cost-type data, x0(k)＝min{xi(k)}。
Further, the formula for calculating the gray correlation coefficient in step S5 is:
Figure BDA0002959795940000041
in which ξi(k) Denotes a gray correlation coefficient between the reference scheme and the i-th evaluation scheme, and ρ denotes a resolution coefficient.
Further, the calculation formula of the gray color correlation degree in step S6 is:
Figure BDA0002959795940000042
wherein gamma (x)0,xi) And representing the grey correlation degree between the first-level disaster node and the corresponding ith second-level disaster node.
The invention has the beneficial effects that:
(1) the invention firstly provides a method for quantitatively evaluating the relevance between the primary disaster and the secondary disaster in the chain disaster by adopting a gray relevance method, and provides an accurate and effective method for researching the relevance between earthquake-dammed lake-flood chain disaster links.
(2) The invention ensures the accuracy of the final analysis result by carrying out dimensionless processing on the established decision matrix.
(3) According to the method, a more accurate reference data column is determined in the decision matrix, other evaluation data and the reference data column are compared and calculated, and a gray correlation coefficient between the other evaluation data and the reference data column is obtained, so that a more accurate evaluation result can be obtained.
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Fig. 1 is a flowchart of an earthquake-dammed lake-flood chained disaster analysis method based on a gray correlation degree according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating formation of an earthquake-dammed lake-flood chain disaster according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It is to be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, not to limit the scope of the invention.
The embodiment of the invention provides a method for analyzing earthquake-dammed lake-flood chain disasters based on grey correlation degrees, which comprises the following steps of S1-S7 as shown in figure 1:
s1, setting the earthquake, the barrier lake and the flood as 3 primary disaster nodes and setting secondary disasters derived from the earthquake, the barrier lake and the flood as secondary disaster nodes according to the earthquake-barrier lake-flood chain disasters of the analysis area.
In the process of chain disaster occurrence, each disaster chain is often not mutually independent, and basically has a certain association and connection, as shown in fig. 2, the earthquake-barrier lake-flood chain disaster is divided into two parts, the left part is a debris flow landslide and mountain collapse area, and the right part is a barrier lake forming area. Through the scouring of landslide and debris flow, the original horizon line can be gradually reduced, and the reduced part can be moved to the downstream along with the scouring of the debris flow and the landslide to form a barrier lake.
And S2, performing attribute division on the secondary disaster node data.
In the embodiment of the invention, the second-level disaster node data can be divided into benefit type data, cost type data and fixed type data. The larger the value of the benefit data is, the better the value of the cost data is, the smaller the value of the cost data is, the better the value of the fixed data is, and the closer the value of the fixed data is to a fixed value, the better the value is.
Benefit type data are expressed as:
X(k)D1＝(x1(k)d1，x2(k)d1，x3(k)d1，...，xm(k)d1)
wherein X (k) D1Representing a benefit type data set, xi(k)d1Representing ith benefit type data after dimensionalization, and the calculation formula is as follows:
Figure BDA0002959795940000051
wherein xi(k) Representing the ith raw data, minx, of the nondimensionalization in the benefit data seti(k) Represents the minimum value in the benefit type dataset, maxxi(k) The maximum value in the benefit type data set is represented, i is 1,2, and.
The cost-type data is expressed as:
X(k)D2＝(x1(k)d2，x2(k)d2，x3(k)d2，...，xm(k)d2)
wherein X (k) D2Representing a cost-type data set, xi(k)d2Representing the ith cost type data after the dimensionalization, and the calculation formula is as follows:
Figure BDA0002959795940000052
wherein xi(k) Representing the ith raw data, minx, not dimensionalized in a cost-based dataseti(k) Represents the minimum value in the cost-based dataset, maxxi(k) The maximum value in the cost-type data set is represented, i is 1, 2., m, k is 1, 2., n, m represents the number of data in the cost-type data set, and n represents the total amount of data in the cost-type data set.
The fixed-type data is represented as:
X(k)D3＝(x1(k)d3，x2(k)d3，x3(k)d3，...，xm(k)d3)
wherein X (k) D3Representing a fixed data set, xi(k)d3Representing the ith fixed type data after the dimensionalization, and the calculation formula is as follows:
Figure BDA0002959795940000061
wherein xi(k) Representing the i-th raw data, gamma, of the non-dimensionalization in a fixed dataseti(k) The fixed value in the ith secondary disaster node is represented, i 1, 2.. and m, k 1, 2.. and n represents the number of data in the permanent type data set, and n represents the total amount of data in the permanent type data set.
And S3, constructing a decision matrix by combining attributes of each secondary disaster node data of the analysis area, and carrying out non-dimensionalization processing on the decision matrix.
The decision matrix constructed in the embodiment of the invention is expressed as:
Figure BDA0002959795940000062
wherein xi(k) Representing a m x n dimensional decision matrix xm*nThe elements in the ith row and the kth column in the middle row are 1,2,.. the m, k is 1,2,.. the n, m represents the data number of the secondary disaster node, and n represents the total data amount of the secondary disaster node.
Because the units of the data of the second-level disaster nodes are different and the meanings of the data are also different, the established decision matrix needs to be subjected to dimensionless processing, so that the accuracy of an analysis result is ensured.
In the embodiment of the present invention, the formula for performing non-dimensionalization processing on the decision matrix is as follows:
Figure BDA0002959795940000063
or
Figure BDA0002959795940000064
Wherein xi(k) Representing a non-dimensionalized pre-processing decision matrix xm*nElement of ith row and kth column, xi' (k) denotes the decision matrix x after dimensionless processingm*nElement of ith row and kth column, xi(k)minRepresenting a decision matrix xm*nMinimum value element of (1), xi(k)maxRepresenting a decision matrix xm*nMaximum value element in (1).
S4, determining a reference data column in the decision matrix, taking an element in the reference data column as a reference scheme, and taking the rest elements in the decision matrix as an evaluation scheme.
In the embodiment of the present invention, the reference data column is represented as:
X0＝(x0(1)，x0(2)，...，x0(n))
the reference data column is an ideal reference standard as far as possible, so that more accurate analysis results can be obtained. When the ith secondary disaster node data belongs to benefit type data, x0(k)＝max{xi(k) When the ith secondary disaster node data belongs to cost-type data, x0(k)＝min{xi(k)}。
And S5, calculating gray correlation coefficients between the reference scheme and each evaluation scheme.
In the embodiment of the invention, the calculation formula of the grey correlation coefficient is as follows:
Figure BDA0002959795940000071
in which ξi(k) The grey correlation coefficient between the reference scheme and the ith evaluation scheme is shown, p represents the resolution coefficient, and p is 0.5 in the embodiment of the invention.
And S6, calculating the grey correlation degree between the primary disaster node and each corresponding secondary disaster node according to the grey correlation coefficient between the reference scheme and each evaluation scheme.
In the embodiment of the invention, the calculation formula of the grey correlation degree is as follows:
Figure BDA0002959795940000072
wherein gamma (x)0,xi) And representing the grey correlation degree between the first-level disaster node and the corresponding ith second-level disaster node.
And S7, performing prediction analysis on the earthquake-dammed lake-flood chain disasters in the analysis area according to the grey correlation degree of the primary disaster nodes and the corresponding secondary disaster nodes.
In the embodiment of the invention, the larger the grey correlation value of the first-level disaster node and the corresponding second-level disaster node is, the higher the possibility of the second-level disaster caused by the first-level disaster node is, namely, the closer the first-level disaster node and the second-level disaster node is; on the contrary, the probability of the second-level disaster caused by the first-level disaster is smaller, that is, the relationship between the first-level disaster and the second-level disaster is sparse.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (10)

1. The earthquake-dammed lake-flood chain disaster analysis method based on the grey correlation degree is characterized by comprising the following steps of:
s1, setting earthquakes, barrier lakes and floods as 3 primary disaster nodes and setting secondary disasters derived from the earthquakes, the barrier lakes and the floods as secondary disaster nodes according to the earthquake-barrier lake-flood chain disasters of the analysis area;
s2, performing attribute division on the secondary disaster node data;
s3, constructing a decision matrix by combining attributes of each secondary disaster node data of the analysis area, and carrying out non-dimensionalization processing on the decision matrix;
s4, determining a reference data column in the decision matrix, taking elements in the reference data column as a reference scheme, and taking the rest elements in the decision matrix as an evaluation scheme;
s5, calculating gray correlation coefficients between the reference scheme and each evaluation scheme;
s6, calculating the grey association degree of the primary disaster node and each corresponding secondary disaster node according to the grey association coefficient between the reference scheme and each evaluation scheme;
and S7, performing prediction analysis on the earthquake-dammed lake-flood chain disasters in the analysis area according to the grey correlation degree of the primary disaster nodes and the corresponding secondary disaster nodes.
2. The earthquake-dammed lake-flood chained disaster analysis method as claimed in claim 1, wherein the secondary disaster node data in step S2 can be divided into benefit type data, cost type data and fixed type data.
3. The earthquake-dammed lake-flood chained disaster analysis method of claim 2, wherein the benefit type data is expressed as:
X(k)D1＝(x1(k)d1，x2(k)d1，x3(k)d1，...，xm(k)d1)
wherein X (k) D1Representing a benefit type data set, xi(k)d1Representing ith benefit type data after dimensionalization, and the calculation formula is as follows:
Figure FDA0002959795930000011
wherein xi(k) Representing the ith raw data, minx, of the nondimensionalization in the benefit data seti(k) Watch (A)Minimum in benefit-type dataset, maxxi(k) The maximum value in the benefit type data set is represented, i is 1,2, and.
4. The seismic-barrage lake-flood chained disaster analysis method of claim 2, wherein the cost-type data is expressed as:
X(k)D2＝(x1(k)d2，x2(k)d2，x3(k)d2，...，xm(k)d2)
wherein X (k) D2Representing a cost-type data set, xi(k)d2Representing the ith cost type data after the dimensionalization, and the calculation formula is as follows:
Figure FDA0002959795930000021
wherein xi(k) Representing the ith raw data, minx, not dimensionalized in a cost-based dataseti(k) Represents the minimum value in the cost-based dataset, maxxi(k) The maximum value in the cost-type data set is represented, i is 1, 2., m, k is 1, 2., n, m represents the number of data in the cost-type data set, and n represents the total amount of data in the cost-type data set.
5. The earthquake-dammed lake-flood chained disaster analysis method of claim 2, wherein the fixed type data is expressed as:
X(k)D3＝(x1(k)d3，x2(k)d3，x3(k)d3，...，xm(k)d3)
wherein X (k) D3Representing a fixed data set, xi(k)d3Representing the ith fixed type data after the dimensionalization, and the calculation formula is as follows:
Figure FDA0002959795930000022
wherein xi(k) Representing the i-th raw data, gamma, of the non-dimensionalization in a fixed dataseti(k) The fixed value in the ith secondary disaster node is represented, i 1, 2.. and m, k 1, 2.. and n represents the number of data in the permanent type data set, and n represents the total amount of data in the permanent type data set.
6. The earthquake-dammed lake-flood chained disaster analysis method as claimed in claim 1, wherein the decision matrix constructed in the step S3 is expressed as:
Figure FDA0002959795930000023
wherein xi(k) Representing a m x n dimensional decision matrix xm*nThe elements in the ith row and the kth column in the middle row are 1,2,.. the m, k is 1,2,.. the n, m represents the data number of the secondary disaster node, and n represents the total data amount of the secondary disaster node.
7. The earthquake-barrier lake-flood chained disaster analysis method as claimed in claim 6, wherein the formula for non-dimensionalizing the decision matrix in step S3 is:
Figure FDA0002959795930000024
or
Figure FDA0002959795930000031
Wherein xi(k) Representing a non-dimensionalized pre-processing decision matrix xm*nElement of ith row and kth column, xi' (k) denotes the decision matrix x after dimensionless processingm*nMiddle ith rowElement of the k-th column, xi(k)minRepresenting a decision matrix xm*nMinimum value element of (1), xi(k)maxRepresenting a decision matrix xm*nMaximum value element in (1).
8. The earthquake-dammed lake-flood chained disaster analysis method as claimed in claim 6, wherein the reference data columns in the step S4 are expressed as:
X0＝(x0(1)，x0(2)，...，x0(n))
when the ith secondary disaster node data belongs to benefit type data, x0(k)＝max{xi(k) When the ith secondary disaster node data belongs to cost-type data, x0(k)＝min{xi(k)}。
9. The earthquake-dammed lake-flood chained disaster analysis method as claimed in claim 8, wherein the grey correlation coefficient in step S5 is calculated by the following formula:
Figure FDA0002959795930000032
in which ξi(k) Denotes a gray correlation coefficient between the reference scheme and the i-th evaluation scheme, and ρ denotes a resolution coefficient.
10. The earthquake-dammed lake-flood chained disaster analysis method as claimed in claim 9, wherein the calculation formula of the grey correlation degree in the step S6 is as follows:
Figure FDA0002959795930000033
wherein gamma (x)0,xi) And representing the grey correlation degree between the first-level disaster node and the corresponding ith second-level disaster node.
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