<trans-title>Evaluation of susceptibility to karst collapse based on the geodetector and analytic hierarchy method: An example of the Zhongliangshan area in Chongqing

WANG Guilin; QIANG Zhuang; CAO Cong; CHEN Yao; HAO Jinyu

doi:10.11932/karst2021y08

Volume 41 Issue 1

Feb. 2022

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Article Navigation > CARSOLOGICA SINICA > 2022 > 41(1): 79-87

WANG Guilin,QIANG Zhuang,CAO Cong,et al.Evaluation of susceptibility to karst collapse based on the geodetector and analytic hierarchy method: An example of the Zhongliangshan area in Chongqing[J].Carsologica Sinica,2022,41(01):79-87. doi: 10.11932/karst2021y08

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Evaluation of susceptibility to karst collapse based on the geodetector and analytic hierarchy method: An example of the Zhongliangshan area in Chongqing

doi: 10.11932/karst2021y08

1.
School of Civil Engineering，Chongqing University， Chongqing 400045， China
2.
National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas， Chongqing 400045， China
3.
Nanjiang Hydrogeological & Engineering Geology Brigade，Chongqing Bureau of Geology and Minerals Exploration， Chongqing 401121， China

Funds:

cstc2019jscx-msxmX0303

KJ2019047

2018YFC1505501

Received Date: 2020-05-30
Publish Date: 2022-02-25

Abstract

Abstract

Karst collapse is a process in which the surface collapses suddenly under the action of various factors in the karst distribution area. As one of the main types of geological disasters in Southwest China， karst collapse mainly damages roads， railways， buildings and surface water， influences the use of agricultural water and land， and causes casualties and property losses. Due to its covertness， suddenness and uncertainty， karst collapse has become an important factor affecting the regional economic development. Therefore， establishing an evaluation model of karst collapse susceptibility that conforms to regional characteristics is of great significance for local the planning of land use and collapse prevention.This research takes the Zhongliangshan area of Chongqing as the study area， and samples 327 collapse points from the field investigation. Based on the factors that may induce the karst collapse， 13 potential impact factors of 6 classes for karst collapse have been preliminarily determined， namely， the topography （elevation， slope， slope direction， surface curvature， section curvature， slope position， and surface roughness）， the geological structure （distance from fault）， strata， hydrogeology conditions （formation rich in water， and terrain humidity index）， human engineering activities （distance from tunnel）， overburden characteristics （soil thickness） and so on， and a geospatial database of the study area has been established by using GIS to process the original data. Given the influence of the sample number in non-collapse points on the selection of impact factors， this study uses three groups of number ratios in different collapse points to analyze the explanatory power （q-statistic in Geodetector） of each factor in the karst collapse area. In order to avoid the fact that the establishment of the pair comparison matrix in the analytic hierarchy process is too tedious and inefficient， resulting from the excessive impact factors， the factor detection has been carried out in three groups of sample points in the study area， and the evaluation factors with greater influence on collapse have been selected quantitatively by the size of q value， based on GIS technology and geographic detector method. According to the principle of analytic hierarchy process and the screening results of subsidence impact factors， the evaluation system of karst collapse susceptibility in the study area has been established by taking the karst collapse susceptibility as the target layer. In order to accurately reflect the important difference among factors and reduce the influence of human experience factors， a pair comparison matrix has been established based on the collapse distribution， impact factor analysis results and q value results of geographical detector. The susceptibility to karst collapse has been evaluated by using the analytic hierarchy process. With the help of the GIS spatial analysis module， the evaluation results have been assigned to grid units， and then the zoning map of the karst collapse susceptibility in the study area is obtained. The results show that as the sample amount changes， there is a degree difference in the importance of impact factors. However， among the three sets of data， strata， formation rich in water， distance from tunnel， elevation and slope are always the factors that have the largest impact on karst collapse. The use of geographic detectors to filter factors can avoid the influence of irrelevant factors， and the prediction accuracy （89.88%） conducted by analytic hierarchy process to zone the karst collapse susceptibility has been significantly improved. The areas with higher probability of collapse mainly distribute in the Jialingjiang formation and Daye formation in the karst trough area.
- karst collapse,
- susceptibility,
- geodetector,
- analytic hierarchy method

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References(19)

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