Geomechanics and Engineering

  • 제36권5호
  • /
  • Pages.441-453
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  • 2024
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
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Prediction of karst sinkhole collapse using a decision-tree (DT) classifier

  • Boo Hyun Nam (Department of Civil Engineering, College of Engineering, Kyung Hee University) ;
  • Kyungwon Park (Department of Civil Engineering, College of Engineering, Kyung Hee University) ;
  • Yong Je Kim (Department of Civil and Environmental Engineering, Lamar University)
  • 투고 : 2023.10.18
  • 심사 : 2024.02.11
  • 발행 : 2024.03.10
⟨ 이전 논문 다음 논문 ⟩

초록

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

키워드

과제정보

The authors thank the Florida Geological Survey (FGS) for sharing the data (sinkhole survey and geological data).

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