Spatial analysis for susceptibility of second-time karst sinkholes: A case study of Jili Village in Guangxi, China

After a big karst sinkhole happened in Jili Village of Guangxi, China, the local government was eager to quantitatively analyze and map susceptible areas of the potential second-time karst sinkholes in order to make timely decisions whether the residents living in the first-time sinkhole areas should move. For this reason, karst sinkholes susceptibility geospatial analysis is investigated using multivariate spatial data, logistic regression model (LRM) and Geographical Information System (GIS). Ten major karst sinkholes related factors, including (1) formation lithology, (2) soil structure, (3) profile curvature, (4) groundwater depth, (5) fluctuation of groundwater level, (6) percolation rate of soil, (7) degree of karst development, (8) distance from fault, (9) distance from the traffic route, and (10) overburden thickness were selected, and then each of factors was classified and quantitated with the three or four levels. The LRM was applied to evaluate which factor makes significant contributions to sinkhole. The results demonstrated that formation lithology, soil structure, profile curvature, groundwater depth, ground water level, percolation rate of soil, and degree of karst development, the distance from fault, and overburden thickness are positive, while one factor, the distance from traffic routes is negative, which is deleted from LRM model. The susceptibility of the potential sinkholes in the study area is estimated and mapped using the solved impact factors. The susceptible degrees of the study area are classified into five levels, very high, high, moderate, low, and ignore susceptibility. It has been found that that both very high and high susceptibility areas are along Datou Hill and the foothills of the study area. This finding is verified by field observations. With the investigations conducted in this paper, it can be concluded that the susceptibility maps produced in this paper are reliable and accurate, and useful as a reference for local governments to make decisions regarding whether or not residents living within sinkhole areas should move. Karst sinkholes susceptibility geospatial analysis is investigated.Ten major karst sinkholes factors were quantitated.Logistic model is used for susceptible sinkhole analysis.A 5-level susceptibility map was created for local government.

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