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http://dx.doi.org/10.7843/kgs.2021.37.12.117

Development of Random Forest Model for Sewer-induced Sinkhole Susceptibility  

Kim, Joonyoung (Disivion of Smart Interdiscplinary Engrg., Hannam Univ.)
Kang, Jae Mo (Korea Institute of Civil Engrg. and Building Technology)
Baek, Sung-Ha (Korea Institute of Civil Engrg. and Building Technology)
Publication Information
Journal of the Korean Geotechnical Society / v.37, no.12, 2021 , pp. 117-125 More about this Journal
Abstract
The occurrence of ground subsidence and sinkhole in downtown areas, which threatens the safety of citizens, has been frequently reported. Among the various mechanisms of a sinkhole, soil erosion through the damaged part of the sewer pipe was found to be the main cause in Seoul. In this study, a random forest model for predicting the occurrence of sinkholes caused by damaged sewer pipes based on sewage pipe information was trained using the information on the sewage pipe and the locations of the sinkhole occurrence case in Seoul. The random forest model showed excellent performance in the prediction of sinkhole occurrence after the optimization of its hyperparameters. In addition, it was confirmed that the sewage pipe length, elevation above sea level, slope, depth of landfill, and the risk of ground subsidence were affected in the order of sewage pipe information used as input variables. The results of this study are expected to be used as basic data for the preparation of a sinkhole susceptibility map and the establishment of an underground cavity exploration plan and a sewage pipe maintenance plan.
Keywords
Prediction; Random forest; Sewer pipe; Sinkhole; Susceptibility;
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