Railway line heavy rainfall disaster risk zoning method based on combined weight analysis method

Abstract

The invention discloses a railway line heavy rainfall disaster risk zoning method based on a combined weight analysis method, and relates to the technical field of railway line heavy rainfall disaster risk assessment, and the method comprises the following steps: determining a heavy rainfall disaster risk evaluation index according to an established railway line heavy rainfall disaster risk model; acquiring data of related three-level indexes by utilizing ArcGIS software; performing region segmentation on the target line, firstly determining the positive and negative directivity of the three-level indexes for each small region, and then performing corresponding normalization processing on the three-level index data; performing subjective weight scoring on the importance of each three-level index by using a traditional analytic hierarchy process; carrying out objective weight assignment on the importance of each three-level index by using an entropy weight method according to the data; according to the method, the entropy weight method is introduced to neutralize the subjectivity of the weight obtained by the analytic hierarchy process, and meanwhile, the data acquisition is also based on the generation of GIS software, so that the final risk division has higher objectivity and accuracy.

Description

Technical Field

[0001] The invention relates to the technical field of railway heavy rainfall disaster risk assessment, and in particular to a heavy rainfall disaster risk zoning method along a railway based on a combined weight analysis method. Background Art

[0002] Heavy rainfall disasters refer to the damage caused to the natural environment and human activities by large amounts of precipitation in a short period of time, usually manifested as floods, landslides, mud-rock flows and other disasters caused by continuous heavy rains. This weather phenomenon can cause damage to infrastructure (such as railways, roads, bridges), inundation of farmland, urban waterlogging, and even casualties. The frequency and severity of heavy rainfall disasters vary depending on regional climatic conditions, and are more common in mountainous areas with complex terrain.

[0003] China's railways are developing rapidly, but the increase in the speed of high-speed rail will inevitably face more serious railway disasters. In terms of meteorology, heavy rainfall, strong winds, blizzards, and lightning are all extremely harmful to the normal operation of trains. Therefore, it is extremely necessary to study the characteristics of heavy rainfall disasters along the railway, its risk assessment and zoning, and explore the evolution of natural disasters around the line, so that people can effectively warn and avoid disasters and ensure the safety of passengers.

[0004] The analytic hierarchy process (AHP) is a multi-criteria decision-making method proposed by Thomas Satie, an American operations researcher in the 1970s. This method decomposes complex problems into sub-problems at different levels, and uses expert scoring to make subjective judgments, combined with mathematical models, to derive the relative importance of indicators at each level. AHP is widely used in risk zoning for flood risk zoning, earthquake risk assessment, and landslide prediction. However, AHP also has limitations, such as being greatly affected by the subjective judgment of experts, and when dealing with complex problems, data acquisition and matrix construction are also relatively complicated. Therefore, the current risk zoning along the railway based on the analytic hierarchy process is too subjective and often ignores the importance of data. Summary of the invention

[0005] The purpose of the present invention is to provide a method for zoning heavy rainfall disaster risks along railways based on a combined weight analysis method, so as to solve the problems existing in the prior art mentioned in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The heavy rainfall disaster risk zoning method along the railway based on the combined weight analysis method includes the following steps:

[0008] S1: Determine the heavy rainfall disaster risk assessment index based on the established heavy rainfall disaster risk model along the railway;

[0009] S2: Use ArcGIS software to obtain data on relevant third-level indicators;

[0010] S3: Divide the target line into regions, and for each small region, first determine the positive and negative directions of the three-level indicators, and then perform corresponding normalization processing on the three-level indicator data;

[0011] S4: Use the traditional analytic hierarchy process to subjectively weight the importance of each level 3 indicator;

[0012] S5: Objectively assign weights to the importance of each level 3 indicator using the entropy weight method based on the data;

[0013] S6: Combining the subjective and objective weight values ​​obtained by the traditional analytic hierarchy process and the entropy weight method to obtain a combined weight;

[0014] S7: Calculate the evaluation index of the second and third level indicators of each small area, so as to obtain the risk of heavy rainfall disasters along the railway;

[0015] S8: Use the natural breakpoint method to grade the risk of heavy rainfall disasters along the entire line.

[0016] Preferably, in S1, the hierarchical structure of the heavy rainfall disaster risk model along the railway is divided into a target layer, a criterion layer and a scenario layer, wherein the target layer is the risk of heavy rainfall disasters, the criterion layer includes disaster susceptibility and disaster vulnerability, wherein disaster susceptibility includes the risk of disaster-causing factors and the sensitivity of the disaster-prone environment, and the scenario layer includes rainfall frequency, elevation of the area along the line, slope, GDP, and the completeness of the early warning system.

[0017] Preferably, in S2, the elevation and slope geographical indicators are obtained from the website of the National Geological Center and then output as a csv data set using ArcGIS software.

[0018] Preferably, in S3, if the value of the third-level index is larger, the risk of heavy rainfall disasters along the railway is higher, then the index is a positive indicator, otherwise it is a negative indicator. The normalization processing formula of the positive index is: The normalization formula for negative indicators is: Among them, D ij Refers to the normalized value of the ith indicator, A ij refers to the i-th value of the j-th indicator, i min Refers to the minimum value of the i-th index, i max Refers to the maximum value among the i-th index.

[0019] Preferably, in S4, the specific steps of the analytic hierarchy process are:

[0020] S41: Classification and assignment of risk indicators of some factors: Assign values ​​to indicators that are not convenient to display in numerical values;

[0021] S42: Constructing the judgment matrix: For indicators A1 to A1 in the criterion layer and the solution layer n , pairwise comparisons were performed using a 1-9 scale to quantify their relative importance;

[0022] S43: Calculate relative weight values: Based on the data set, calculate the subjective weight coefficients for the hazard risk of the hazard-causing factor, the sensitivity of the disaster-prone environment, and the vulnerability to disasters;

[0023] S44: Calculate the consistency ratio: When the consistency ratio is less than 0.1, the consistency of the matrix can be ensured to be within a reasonable range, and the obtained weights are reasonable.

[0024] Preferably, in S5, the specific steps of assigning objective weights to the secondary indicators are:

[0025] S51: Obtain probability matrix P:

[0026]

[0027] S52: Calculate the information entropy E of index j j , the formula is:

[0028]

[0029] Among them, the value of k is 1 / lnn;

[0030] S53: Finally, the objective weight of the weight index is calculated, and the formula is as follows:

[0031]

[0032] Preferably, in S6, the specific steps of evaluating the combined weight are:

[0033] S61: Find the distance between the subjective and objective coefficients W1 and W2. The formula is:

[0034]

[0035] S62: The distribution coefficients for the two are α and β respectively. The relationship between them and the distance d is as follows:

[0036] d 2 =(α-β) 2

[0037] α+β=1

[0038] S63: The formula for calculating the final weight coefficient is as follows:

[0039] W=αW1+βW2

[0041] Preferably, the specific steps of S7 are:

[0042] S71: Calculate the three-level indicators using weighted comprehensive calculation:

[0043] Obviously, the root cause of heavy rainfall disasters is rainstorms. It is known that rainfall exceeding 20 mm / h will have an impact on the railway. Let the disaster factor be expressed as:

[0044]

[0045] Where, T i They are respectively expressed as the frequency of 20mm≤hourly rainfall≤30mm, the frequency of 30mm≤hourly rainfall≤40mm, the frequency of 40mm≤hourly rainfall≤50mm, the frequency of 50mm≤hourly rainfall≤70mm and the frequency of hourly rainfall≥70mm, t i are their corresponding secondary weight coefficients;

[0046] The sensitivity of the disaster-prone environment can be expressed as:

[0047]

[0048] Where W i They represent the elevation, slope, soil type and vegetation coverage of the line location, respectively. i are their weight coefficients respectively;

[0049] The expression of disaster vulnerability is:

[0050]

[0051] Where U i They are respectively represented by the high-speed rail mileage, passenger volume, regional GDP index, number of medical institutions and early warning system in the region, c i is the corresponding weight coefficient;

[0052] S72: Calculate secondary indicators:

[0053] S j =tP+bE

[0054] In the formula, P represents the danger of disaster-causing factors, which mainly refers to the frequency of rainfall, E represents the sensitivity of the disaster-prone environment, and t and b are the weight coefficients of the corresponding indicators respectively;

[0055] S73: Calculate the first-level index:

[0056] R j =S j ×Vj

[0057] In the formula, R j represents the risk of heavy rainfall disaster in the jth region, S j and V j They represent the disaster susceptibility and disaster vulnerability of the area respectively.

[0058] Preferably, in S8, according to the obtained heavy rainfall disaster risk R j , carry out disaster risk zoning for the line.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention introduces the entropy weight method to neutralize the subjectivity of the weights obtained by the hierarchical analysis method. At the same time, the acquisition of data is also based on the generation of GIS software, so that the final risk zoning has higher objectivity and accuracy. Taking the Beijing-Zhangjiakou Railway as an example, the test results show that the areas with the highest risk along the railway are distributed in the Badaling to Nankou section, Qinglongqiao section and other places, which basically conforms to the actual disaster situation. Compared with the single hierarchical analysis method, it has higher accuracy and provides a certain reference for better establishing disaster warning and improving rescue systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Flow chart of the method of the present invention.

[0062] Figure 2 This is a railway heavy rainfall disaster risk zoning model based on the combined weight method of the present invention.

[0063] Figure 3 This is the elevation and slope map of the Beijing-Zhangjiakou Railway obtained by the present invention using GIS.

[0064] Figure 4 This is an example map of the susceptibility zone for heavy rainfall disasters on the Beijing-Zhangjiakou Railway.

[0065] Figure 5 This is an example map of the risk zoning for heavy rainfall disasters on the Beijing-Zhangjiakou Railway. DETAILED DESCRIPTION

[0066] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0067] See also Figure 1-5 , the present invention provides the following technical solutions:

[0068] The heavy rainfall disaster risk zoning method along the railway based on the combined weight analysis method includes the following steps:

[0069] S1: Determine the heavy rainfall disaster risk assessment indicators based on the established heavy rainfall disaster risk model along the railway; Figure 2 As shown in the figure, the hierarchical structure of the heavy rainfall disaster risk model along the railway is divided into the target layer, the criterion layer and the scheme layer. The target layer is the first-level indicator disaster risk R j The criterion layer includes the secondary indicator disaster susceptibility S j and Vulnerability j ,The scheme layer includes three levels of indicators such as rainfall frequency, elevation, slope, and high-speed rail mileage.

[0070] S2: Use ArcGIS software to obtain data on related three-level indicators; the elevation and slope geographical indicators are obtained from the website of the National Geological Center and then exported as csv data sets using ArcGIS software; the elevation and slope data along the railway can be obtained by combining Arcmap software with 30mDEM output. The elevation and slope along the Beijing-Zhangjiakou Railway are as follows: Figure 3 As shown; the soil type and vegetation coverage rate shp data are taken from the National Frozen Soil, Glacier and Desert Science Research Center and the Natural Environment and Resources Science Center respectively. The output data is accurate and concise, corresponding one-to-one with the relative indicators, reducing redundancy and facilitating calculation.

[0071] S3: Divide the target line into regions. For each small region, first determine the positive and negative directions of the three-level indicators, and then perform corresponding normalization on the three-level indicator data. If the value of the three-level indicator is larger, the risk of heavy rain disasters along the railway is higher, then the indicator is a positive indicator, otherwise it is a negative indicator. For example, the higher the vegetation coverage rate, the lower the disaster risk is, and it is a negative driving indicator. The normalization formula for positive indicators is: The normalization formula for negative indicators is: Among them, D ij Refers to the normalized value of the ith indicator, A ij refers to the i-th value of the j-th indicator, i min Refers to the minimum value of the i-th index, i max Refers to the maximum value among the i-th index.

[0072] S4: Use the traditional analytic hierarchy process (AHP) to subjectively weight the importance of each level 3 indicator. The specific steps of the analytic hierarchy process are as follows:

[0073] S41: Classification and assignment of risk indicators for some factors: Assign values ​​to indicators that are not convenient to display in numerical values, such as soil type, completeness of early warning system, etc., as shown in Table 1:

[0074] Table 1 Classification and assignment of hazard indicators

[0075]

[0076] S42: Constructing the judgment matrix: For indicators A1 to A1 in the criterion layer and the solution layer n , pairwise comparisons were performed using a 1-9 scale to quantify their relative importance;

[0077]

[0078] In this example, two indicators of the same level A i and A j , the correlation coefficient between the i-th indicator and the j-th indicator is A ij .

[0079] Table 2 AHP scale comparison table

[0080]

[0081] S43: Calculate relative weight values: Based on the data set, calculate the subjective weight coefficients for the hazard risk of the disaster-causing factor, the sensitivity of the disaster-prone environment, and the vulnerability to disasters. The calculation results are shown in Table 3:

[0082] Table 3 Subjective weight coefficients of each factor

[0083]

[0084] S44: Calculate the consistency ratio: Only when the consistency ratio is less than 0.1 can the consistency of the matrix be ensured to be within a reasonable range and the weights obtained be reasonable; the CR value of the disaster risk factor column is 0.0076, the CR value of the disaster environment sensitivity column is 0.0352, and the CR value of the disaster vulnerability column is 0.0364, all of which have passed the consistency test.

[0085] S5: According to the data, the entropy weight method is used to objectively assign weights to the importance of each third-level indicator; the specific steps for objective weight assignment of secondary indicators are:

[0086] S51: Obtain probability matrix P:

[0087]

[0088] S52: Calculate the information entropy E of index j j , the formula is:

[0089]

[0090] Among them, the value of k is 1 / lnn;

[0091] S53: Finally, the objective weight of the weight index is calculated, and the formula is as follows:

[0092]

[0093] The calculation results of the objective weight coefficients of each factor are shown in Table 4:

[0094] Table 4 Objective weight coefficients of each factor

[0095]

[0096] S6: Combine the subjective and objective weight values ​​obtained by the traditional analytic hierarchy process and the entropy weight method to obtain a combined weight. The specific steps for evaluating the combined weight are:

[0097] S61: Find the distance between the subjective and objective coefficients W1 and W2. The formula is:

[0098]

[0099] S62: The distribution coefficients for the two are α and β respectively. The relationship between them and the distance d is as follows:

[0100] d 2 =(α-β) 2

[0101] α+β=1

[0102] S63: The formula for calculating the final weight coefficient is as follows:

[0103] W=αW1+βW2

[0105] The calculation results of the combined weight coefficient are shown in Table 5:

[0106] Table 5 Combination weight coefficients of various factors

[0107]

[0108]

[0109] S7: Calculate the evaluation index of the second and third level indicators of each small area, so as to obtain the risk of heavy rainfall disasters along the railway. The specific steps are:

[0110] S71: Calculate the three-level indicators using weighted comprehensive calculation:

[0111] Obviously, the root cause of heavy rainfall disasters is rainstorms. It is known that rainfall exceeding 20 mm / h will have an impact on the railway. Let the disaster factor be expressed as:

[0112]

[0113] Where, T iThey are respectively expressed as the frequency of 20mm≤hourly rainfall≤30mm, the frequency of 30mm≤hourly rainfall≤40mm, the frequency of 40mm≤hourly rainfall≤50mm, the frequency of 50mm≤hourly rainfall≤70mm and the frequency of hourly rainfall≥70mm, t i are their corresponding secondary weight coefficients;

[0114] The sensitivity of the disaster-prone environment can be expressed as:

[0115]

[0116] Where W i They represent the elevation, slope, soil type and vegetation coverage of the line location, respectively. i are their weight coefficients respectively;

[0117] The expression of disaster vulnerability is:

[0118]

[0119] Where U i They are respectively represented by the high-speed rail mileage, passenger volume, regional GDP index, number of medical institutions and early warning system in the region, c i is the corresponding weight coefficient;

[0120] S72: Calculate secondary indicators:

[0121] S j =tP+bE

[0122] Where P represents the danger of disaster-causing factors, which mainly refers to the frequency of rainfall; E represents the sensitivity of the disaster-prone environment; t and b are the weight coefficients of the corresponding indicators respectively; the expert scoring method is used to set t and b to 0.6667 and 0.3333 respectively.

[0123] S73: Calculate the first-level index:

[0124] R j =S j ×V j

[0125] In the formula, R j represents the risk of heavy rainfall disaster in the jth region, S j and V j They represent the disaster susceptibility and disaster vulnerability of the area respectively.

[0126] S8: Use the natural breakpoint method to classify the risk of heavy rainfall disasters on the entire line. j , the disaster risk zoning of the line was carried out, and finally the disaster susceptibility and disaster risk zoning of the Beijing-Zhangjiakou Railway was obtained; Figure 4 As shown in Figure 2, extremely high-risk areas are concentrated in the Badaling to Nankou section, the Qinglongqiao area, and the Kangzhuang to Xiahuayuan section. Figure 5 As shown, extremely high-risk areas are concentrated in the northwest of Beijing’s main urban area, the Badaling to Nankou section, and the Qiaodong area.

[0127] The present invention introduces the entropy weight method to neutralize the subjectivity of the weights obtained by the hierarchical analysis method. At the same time, the acquisition of data is also based on the generation of GIS software, so that the final risk zoning has higher objectivity and accuracy. Taking the Beijing-Zhangjiakou Railway as an example, the test results show that the areas with the highest risks along the railway are distributed in the Badaling to Nankou section, Qinglongqiao section and other places, which basically conforms to the actual disaster situation. Compared with the single hierarchical analysis method, it has higher accuracy and provides a certain reference for better establishing disaster warning and improving rescue systems.

[0128] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for zoning the risk of heavy rainfall disasters along railways based on the combined weight analysis method is characterized by: The following steps are involved: S1: Determine the heavy rainfall disaster risk assessment index based on the established heavy rainfall disaster risk model along the railway; S2: Use ArcGIS software to obtain data on relevant third-level indicators; S3: Divide the target line into regions, and for each small region, first determine the positive and negative directions of the three-level indicators, and then perform corresponding normalization processing on the three-level indicator data; S4: Use the traditional analytic hierarchy process to subjectively weight the importance of each level 3 indicator; S5: Objectively assign weights to the importance of each level 3 indicator using the entropy weight method based on the data; S6: Combining the subjective and objective weight values ​​obtained by the traditional analytic hierarchy process and the entropy weight method to obtain a combined weight; S7: Calculate the evaluation index of the second and third level indicators of each small area, so as to obtain the risk of heavy rainfall disasters along the railway; S8: Use the natural breakpoint method to grade the risk of heavy rainfall disasters along the entire line.

2. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 1 is characterized in that: In S1, the hierarchical structure of the heavy rainfall disaster risk model along the railway is divided into a target layer, a criterion layer and a scenario layer, wherein the target layer is the risk of heavy rainfall disasters, the criterion layer includes disaster susceptibility and disaster vulnerability, wherein disaster susceptibility includes the risk of disaster-causing factors and the sensitivity of the disaster-prone environment, and the scenario layer includes rainfall frequency, elevation of the area along the line, slope, GDP, and the completeness of the early warning system.

3. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 1 is characterized in that: In S2, the elevation and slope geographical indicators are obtained from the website of the National Geological Center and then exported as a csv data set using ArcGIS software.

4. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 1 is characterized in that: In S3, if the value of the third-level index is larger, the risk of heavy rainfall disasters along the railway is higher, then the index is a positive index, otherwise it is a negative index. The normalization processing formula of the positive index is: The normalization formula for negative indicators is: Among them, D ij Refers to the normalized value of the ith indicator, A ij refers to the i-th value of the j-th indicator, i min Refers to the minimum value of the i-th index, i max Refers to the maximum value among the i-th index.

5. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 2 is characterized in that: In S4, the specific steps of the hierarchical analysis method are: S41: Classification and assignment of risk indicators of some factors: Assign values ​​to indicators that are not convenient to display in numerical values; S42: Constructing the judgment matrix: For indicators A1 to A1 in the criterion layer and the solution layer n , pairwise comparisons were performed using a 1-9 scale to quantify their relative importance; S43: Calculate relative weight values: Based on the data set, calculate the subjective weight coefficients for the hazard risk of the hazard-causing factor, the sensitivity of the disaster-prone environment, and the vulnerability to disasters; S44: Calculate the consistency ratio: When the consistency ratio is less than 0.1, the consistency of the matrix can be ensured to be within a reasonable range, and the obtained weights are reasonable.

6. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 2 is characterized in that: In S5, the specific steps of assigning objective weights to the secondary indicators are: S51: Obtain probability matrix P: S52: Calculate the information entropy E of index j j , the formula is: Among them, the value of k is 1 / lnn; S53: Finally, the objective weight of the weight index is calculated, and the formula is as follows:

7. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 1 is characterized in that: In S6, the specific steps of evaluating the combined weight are: S61: Find the distance between the subjective and objective coefficients W1 and W2. The formula is: S62: The distribution coefficients for the two are α and β respectively. The relationship between them and the distance d is as follows: d 2 ＝(α-β) 2 α+β＝1 S63: The formula for calculating the final weight coefficient is as follows: W=αW1+βW2 8. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 2 is characterized by: The specific steps of S7 are: S71: Calculate the three-level indicators using weighted comprehensive calculation: Obviously, the root cause of heavy rainfall disasters is rainstorms. It is known that rainfall exceeding 20 mm / h will have an impact on the railway. Let the disaster factor be expressed as: Where, T i They are respectively expressed as the frequency of 20mm≤hourly rainfall≤30mm, the frequency of 30mm≤hourly rainfall≤40mm, the frequency of 40mm≤hourly rainfall≤50mm, the frequency of 50mm≤hourly rainfall≤70mm and the frequency of hourly rainfall≥70mm, t i are their corresponding secondary weight coefficients; The sensitivity of the disaster-prone environment can be expressed as: Where W i They represent the elevation, slope, soil type and vegetation coverage of the line location, respectively. i are their weight coefficients respectively; The expression of disaster vulnerability is: Where U i They are respectively represented by the high-speed rail mileage, passenger volume, regional GDP index, number of medical institutions and early warning system in the region, c i is the corresponding weight coefficient; S72: Calculate secondary indicators: S j =tP+bE In the formula, P represents the danger of disaster-causing factors, which mainly refers to the frequency of rainfall, E represents the sensitivity of the disaster-prone environment, and t and b are the weight coefficients of the corresponding indicators respectively; S73: Calculate the first-level index: R j =S j ×V j In the formula, R j represents the risk of heavy rainfall disaster in the jth region, S j and V j They represent the disaster susceptibility and disaster vulnerability of the area respectively.

9. The method for zoning the heavy rainfall disaster risk along the railway based on the combined weight analysis method according to claim 1 is characterized in that: In S8, according to the obtained heavy rainfall disaster risk R j , carry out disaster risk zoning for the line.

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