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CN-121998425-A - Comprehensive assessment method for flood risk of subway station

CN121998425ACN 121998425 ACN121998425 ACN 121998425ACN-121998425-A

Abstract

The invention discloses a comprehensive assessment method for flood risks of subway stations, which comprises the steps of 1, obtaining multi-source data, preprocessing the multi-source data to ensure spatial consistency, availability and accuracy of the data, 2, processing the multi-source data obtained in the step 1 to obtain secondary indexes, carrying out standardization processing on the secondary indexes, calculating weights W j of the standardized secondary indexes through an entropy weight method, carrying out weighted summation on the secondary indexes according to the weights to obtain primary indexes, and 3, sequentially calculating a flooding risk value and a damage risk value based on the primary indexes obtained in the step 2 to obtain a comprehensive flood risk value. The system disclosed by the invention is used for combining four major indexes of flood frequency, subway station design, exposition and vulnerability from the subway station view angle for the first time, constructing a multi-factor comprehensive evaluation method and forming a systematic and integrated risk evaluation framework.

Inventors

  • HUANG JINHUI
  • MA YISHUAI

Assignees

  • 南开大学

Dates

Publication Date
20260508
Application Date
20260127

Claims (10)

  1. 1. The comprehensive assessment method for the flood risk of the subway station is characterized by comprising the following steps of: Step 1, multi-source data are obtained and preprocessed to ensure the spatial consistency, availability and accuracy of the data; Step 2, carrying out data processing on the multi-source data obtained in the step 1 to obtain secondary indexes, carrying out standardization processing on the secondary indexes, calculating the weight W j of each standardized secondary index through an entropy weight method, and carrying out weighted summation on the secondary indexes according to the weights to obtain primary indexes; And step 3, calculating a flooding risk value and a damage risk value in sequence based on the first-level index obtained in the step 2, so as to obtain a flood risk comprehensive value.
  2. 2. The subway station flood risk comprehensive assessment method according to claim 1, wherein in step 1, the multi-source data includes remote sensing data, geospatial data and socioeconomic data.
  3. 3. The subway station flood risk comprehensive assessment method according to claim 2, wherein in step 1, the remote sensing data includes SAR image data, DEM data and HAND data; In the step 1, the geospatial data comprises POI data, building vector data, subway line vector data and drainage pump station position data; in step 1, the socioeconomic data includes population density data and night light data.
  4. 4. The comprehensive assessment method for flood risk of subway stations according to claim 3 is characterized in that in the step 1, SAR image data is characterized in that spatial resolution is 10-100 m, preferably, the SAR image data adopts sentinel number one data, the spatial resolution is 10 meters, the time scale is the assessment year, and the SAR image data is obtained from a remote sensing data sharing platform and used for flood range identification and frequency statistics; the DEM data is that the spatial resolution is 10-90 m, and the DEM data is obtained from a geospatial data mechanism and is used for calculating the terrain parameters of gradient and elevation; the HAND data, wherein the spatial resolution is 10-90 m, is acquired from a remote sensing data platform and is used for topography correction of flood classification results; In the step1, POI data comprise subway station position points, subway entrance and exit position points, hospital position points and fire station position points, wherein the time scale is evaluation year, and the POI data are obtained from a map service platform and used for calculating related indexes of subway station design and emergency capability; The building vector data is obtained from an open source map platform or a city planning data platform for counting the number density of buildings, wherein the time scale is the estimated year or the previous year; the subway line vector data is obtained from a map service platform or an urban rail transit operation mechanism by taking the time scale as an evaluation year and used for calculating the density of the subway line; the water drainage pump station position data is obtained from an open data platform of the urban government and calculated according to the time scale of the estimated year and related indexes of the emergency water drainage capacity; In the step 1, the population density data is that the spatial resolution is 100-1000 m, preferably LandScan global population database data are adopted, the spatial resolution is 1km, and the time scale is the estimated year, and the population density data are used for representing the population aggregation degree of the region; The night lamplight data are characterized by the economic activity intensity of a region, the spatial resolution is 100-1000 m, and preferably, the night lamplight data are VIIRS noctilucent year average data which are acquired from a Google EARTH ENGINE platform and are subjected to time scale coverage and evaluation.
  5. 5. The subway station flood risk comprehensive assessment method according to claim 1, wherein in step 1, the preprocessing comprises unified coordinate system, format conversion, region clipping and noise removal which are sequentially performed; the unified coordinate system is that all data are converted to the same geographic coordinate system, so that spatial offset is avoided; The format conversion is to unify the data formats, so that the subsequent software processing is convenient; Preferably, SAR image data, DEM data, HAND data, population density data and night light data are raster data, POI data, building vector data, subway line vector data and drainage pump station position data are vector data, wherein the raster data are unified into GeoTIFF format, and the vector data are unified into Shapefile format; cutting all data to the evaluation range according to the boundary of the target evaluation area, so as to reduce the data redundancy; The noise removal is to remove noise from SAR images by adopting median filtering, wherein the filtering radius is set to be 5-20 m, and the population density data and night lamplight data are subjected to smoothing processing by adopting mean filtering so as to weaken data noise.
  6. 6. The comprehensive assessment method of subway station flood risk according to claim 1, wherein in the step 2,4 parallel primary indexes are flood frequency FF, subway station design HS, exposure E and vulnerability V; Flood frequency FF has no secondary indicator; the subway station design HS comprises 3 secondary indexes including subway entrance and exit elevation ME, subway entrance and exit gradient MES and subway entrance and exit quantity NSE; the exposure E comprises 4 secondary indexes including population density PD, night light intensity NTL, building number density BDC and subway line density MLD; The vulnerability V includes 3 secondary indicators, namely a linear distance DTH to the nearest hospital, a linear distance DTFS to the nearest fire station, and a linear distance DTPS to the nearest drainage pump station.
  7. 7. The subway station flood risk comprehensive assessment method according to claim 1, wherein in step 2, the data processing specifically comprises: establishing a flood classification model through a random forest model, classifying all SAR image data to obtain flood inundation ranges in images of each time phase, carrying out terrain correction on the flood inundation ranges by using DEM data and HAND data, and carrying out pixel-by-pixel statistics on all the flood inundation ranges to calculate the flood frequency of each pixel; Fitting subway station position points in the POI data into flood frequency space distribution data, and setting a round buffer zone with the radius of 300-800 m for each subway station position point; Coupling subway entrance and exit position points in the POI data with the DEM data, extracting the elevation value of each subway entrance and exit based on the DEM data, and averaging the elevation values of all the entrances and exits of the same subway station to obtain subway entrance and exit elevation ME; Calculating gradient data based on DEM data through a GIS tool, extracting gradient values of all subway entrances and exits, and averaging the gradient values of all entrances and exits of the same subway station to obtain subway entrance and exit gradient MES; counting the number of entrances and exits of each subway station through subway entrance and exit position points in the POI data to obtain the number NSE of subway entrances and exits; Fitting subway station position points in the POI data into population density data, and setting a round buffer zone with the radius of 300-800 m for each subway station position point; fitting subway station position points in the POI data into night light data, and setting a round buffer zone with the radius of 300-800 m for each subway station position point; Fitting subway station position points in the POI data into building vector data, and setting a round buffer zone with the radius of 300-800 m for each subway station position point; Fitting subway station position points in the POI data into subway line vector data, and setting a round buffer zone with the radius of 300-800 m for each subway station position point; based on the hospital position points and the fire station position points in the POI data, calculating the linear distance DTH between each subway station position point and the nearest hospital and the linear distance DTFS between each subway station position point and the nearest fire station through a GIS tool; Based on the position data of the waterlogging pumping stations, calculating the linear distance DTPS between each subway station position point and the nearest waterlogging pumping station.
  8. 8. The subway station flood risk comprehensive assessment method according to claim 1, wherein in step 2, the normalization process specifically comprises: The secondary indexes with positive correlation comprise subway entrance and exit quantity NSE, population density PD, night light intensity NTL, building quantity density BDC, subway line density MLD, a linear distance DTH with the nearest hospital, a linear distance DTFS with the nearest fire station and a linear distance DTPS with the nearest drainage pump station, and the standardized formulas are as follows: (1) The secondary indexes with negative correlation comprise subway entrance and exit elevation ME and subway entrance and exit gradient MES, original data are required to be inverted, the influence directions of all indexes on flood risks are ensured to be consistent, and a standardized formula is as follows: (2) (3) In the formulas (1) - (3), x is the primary value of the secondary index, x rev is the inverse value of the index with negative correlation, x std is the normalized value, x min 、x max is the minimum value and the maximum value of the primary value of the secondary index, and x rev,min 、x rev,max is the minimum value and the maximum value of the inverse value; in the step 2, the entropy weight method specifically comprises the following steps: Setting the evaluation object as m subway stations, setting the total number of secondary indexes as n, and setting the index matrix as X= (X ij ) m multiplied by n, wherein X ij is the standardized value of the j secondary indexes of the i station; For each secondary index, calculating the proportion P ij of the index value of the ith station to all values of the index, wherein the formula is as follows: (4) If it is Let P ij = 1/m, avoid nonsensical logarithmic calculation; The entropy value E j of the j-th secondary index is calculated based on P ij , and the formula is as follows: (5) If P ij =0, let ; The weight W j of the j-th secondary index is calculated based on the entropy value E j , and the formula is as follows: (6) In the formula (6), the amino acid sequence of the compound, The smaller the entropy value E j , the greater the dispersion of the index data, the more significant the contribution to risk assessment, and the higher the weight W j .
  9. 9. The subway station flood risk comprehensive assessment method according to claim 1, wherein in step 2, the weighted summation is specifically: carrying out standardized treatment on the elevation ME of the subway entrance and the exit, the gradient MES of the subway entrance and the number NSE of the subway entrance and the exit according to the formulas (1) - (3) to obtain MEstd, MESstd, NSEstd, and combining the weights WME, WMES, WNSE determined by the formulas (4) - (6), so that the calculation formula of the design HS of the subway station is as follows: (7) the second-level index population density PD, night light intensity NTL, building number density BDC and subway line density MLD are subjected to standardized treatment according to the formulas (1) - (3) to obtain PDstd, NTLstd, BDCstd, MLDstd, and the weight WPD, WNTL, WBDC, WMLD determined by combining the formulas (4) - (6) is calculated as follows: (8) the linear distance DTH between the secondary index and the nearest hospital, the linear distance DTFS between the secondary index and the nearest fire station and the linear distance DTPS between the secondary index and the nearest drainage pump station are standardized according to the formulas (1) - (3) to obtain DTHstd, DTFSstd, DTPSstd, and the weight WDTH, WDTFS, WDTPS determined by the formulas (4) - (6) is combined, so that the calculation formula of the vulnerability V is as follows: (9)。
  10. 10. The subway station flood risk comprehensive assessment method according to claim 1, wherein in step 3, the flooding risk value M1 focuses on whether the flood can submerge the core risk of the station, only the interaction between disaster source intensity and facility resistance is considered, and the method is suitable for a scene of station flooding risk screening, and the calculation formula is as follows: M1=FF×HS (10) In the formula (10), FF is the flood frequency calculated in the step (2) and represents the disaster source intensity, HS is the subway station design calculated in the step (2) and represents the flood resistance of the station; In step 3, the damage risk value M2 introduces the exposure E based on the flooding risk value M1, further quantifies the possible loss caused by flooding, and is suitable for the scenario of potential loss evaluation, and the calculation formula is as follows: M2=FF×HS×E (11) In the formula (11), FF is the flood frequency calculated in the step (2) and represents the disaster source intensity, HS is the subway station design calculated in the step (2) and represents the flood resisting capability of the station, E is the exposition calculated in the step (2) and represents the population, economy and facility density degree and the station importance of the periphery of the station, and the product of the three reflects the potential loss risk caused by flooding; In step 3, the flood risk comprehensive value M3 introduces vulnerability V based on the damage risk value M2, and represents comprehensive risks of flood occurrence, flooding, loss and response of the subway station, and is suitable for a scene formulated by a comprehensive disaster prevention and reduction scheme, and the calculation formula is as follows: M3=FF×HS×E×V (12) In the formula (12), FF represents the flood frequency calculated in the step 2 and represents the disaster source intensity, HS represents the subway station design calculated in the step 2 and represents the flood resisting capability of the subway station, E represents the population, economy and facility density degree and the importance of the subway station, V represents the vulnerability calculated in the step 2 and represents the emergency facility guaranteeing capability of the subway station, and the product of the four represents the flood comprehensive risk of the subway station completely.

Description

Comprehensive assessment method for flood risk of subway station Technical Field The invention relates to the field of urban flood risk assessment and infrastructure toughness planning, in particular to a comprehensive subway station flood risk assessment method. Background With the progress of global climate change and urbanization aggravated, urban inland inundation and extreme rainfall events are increasingly frequent, and a subway system is used as an important backbone of urban public transportation and faces unprecedented risk challenges in front of floods. Subway stations are usually located in a traffic hub core area, underground structures are complex, entrances and exits are numerous, the subway stations are very easy to become key areas for ponding collection and backflow, and once waterlogging occurs, traffic operation is affected, and casualties and economic losses can be caused. In recent years, academia has made positive progress in urban flood risk assessment. The document "Wang, Y., Zhang, Q., Lin, K., Liu, Z., Liang, Y., Liu, Y., Li, C., 2024. A novel framework for urban flood risk assessment: Multiple perspectives and causal analysis. Water Res. 256, 121591." proposes an urban flood multi-index assessment model based on an IPCC risk framework, and factors such as adaptability are included. The Urban Flood Risk Triangular Index (UFRTI) is constructed in literature "Seemuangngam, A., Lin, H.-L., 2024. The impact of urbanization on urban flood risk of Nakhon Ratchasima, Thailand. Appl. Geogr. 162, 103152.", and the space-time assessment of urban flood is realized by combining Bivariate LISA with K-means clustering. The literature "Tang, X., Huang, X., Tian, J., Pan, S., Ding, X., Zhou, Q., Sun, C., 2024. A novel framework for the spatiotemporal assessment of urban flood vulnerability. Sustain. Cities Soc. 109, 105523." proposes an urban flood vulnerability assessment framework based on machine learning and urban vitality indexes, which can quantify urban waterlogging susceptibility and vulnerable subjects. A subway flood risk assessment method fused with a city flood inundation model, a random forest algorithm and a Triangle Fuzzy Number Analytic Hierarchy Process (TFNAHP) is developed in literature "Guan, X., Yu, F., Xu, H., Li, C., Guan, Y., 2024. Flood risk assessment of urban metro system using random forest algorithm and triangular fuzzy number based analytical hierarchy process approach. Sustain. Cities Soc. 109, 105546.". Based on the complex network theory in literature "Gong, Y., Xu, X., Tian, K., Li, Z., Wang, M., Li, J., 2024. Subway station flood risk management level analysis. J. Hydrol. 638, 131473.", the flood management level of the subway system is evaluated by combining passenger flow statistics and a flood risk map. Although the above studies provide a variety of technical paths in urban flood risk assessment, there are certain limitations. On one hand, the existing method focuses on single dimensions such as surface water accumulation, vulnerability or emergency management, lacks a systematic and integrated multidimensional comprehensive assessment framework for punctiform facilities such as subway stations, and on the other hand, the existing research has not fully utilized flood frequency information contained in remote sensing time sequence data, and also fails to organically combine multiple factors such as site design characteristics, exposure degree and emergency response capability, so as to form a comprehensive assessment model for important traffic nodes. Disclosure of Invention Aiming at the defects of the prior art, the invention provides a comprehensive assessment method for the flood risk of a subway station. The technical scheme for solving the technical problems is that the invention provides a comprehensive assessment method for flood risk of subway stations, which is characterized by comprising the following steps: Step 1, multi-source data are obtained and preprocessed to ensure the spatial consistency, availability and accuracy of the data; Step 2, carrying out data processing on the multi-source data obtained in the step 1 to obtain secondary indexes, carrying out standardization processing on the secondary indexes, calculating the weight W j of each standardized secondary index through an entropy weight method, and carrying out weighted summation on the secondary indexes according to the weights to obtain primary indexes; And step 3, calculating a flooding risk value and a damage risk value in sequence based on the first-level index obtained in the step 2, so as to obtain a flood risk comprehensive value. Compared with the prior art, the invention has the beneficial effects that: (1) The system integrates four indexes of flood frequency, subway station design, exposition and vulnerability from the subway station view angle for the first time, and a multi-factor comprehensive evaluation method is constructed, so that the defect that the existing evalua