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http://dx.doi.org/10.14481/jkges.2022.23.4.5

Comparison of Machine Learning Models to Predict the Occurrence of Ground Subsidence According to the Characteristics of Sewer  

Lee, Sungyeol (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Jinyoung (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Kang, Jaemo (Department of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
Baek, Wonjin (Department of Rural and Biosystems Engineering, Chonnam National University)
Publication Information
Journal of the Korean GEO-environmental Society / v.23, no.4, 2022 , pp. 5-10 More about this Journal
Abstract
Recently, ground subsidence has been continuously occurring in downtown areas, threatening the safety of citizens. Various underground facilities such as water and sewage pipelines and communication pipelines are buried under the road. It is reported that the cause of ground subsidence is the deterioration of various facilities and the reckless development of the underground. In particular, it is known that the biggest cause of ground subsidence is the aging of sewage pipelines. As an existing study related to this, several representative factors of sewage pipelines were selected and a study to predict the risk of ground subsidence through statistical analysis has been conducted. In this study, a data SET was constructed using the characteristics of OO city's sewage pipe characteristics and ground subsidence data, The data set constructed from the characteristics of the sewage pipe of OO city and the location of the ground subsidence was used. The goal of this study was to present a classification model for the occurrence of ground subsidence according to the characteristics of sewage pipes through machine learning. In addition, the importance of each sewage pipe characteristic affecting the ground subsidence was calculated.
Keywords
Ground Subsidence; Sewer; Machine Learning; Ground subsidence prediction model;
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Times Cited By KSCI : 2  (Citation Analysis)
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