A Comparative Analysis of Slope Failure Prediction Using a Statistical and Machine Learning Approach on Displacement Data: Introducing a Tailored Performance Metric

被引:4
作者
Chaulagain, Suresh [1 ]
Choi, Junhyuk [2 ]
Kim, Yongjin [3 ]
Yeon, Jaeheum [1 ]
Kim, Yongseong [1 ]
Ji, Bongjun [1 ]
机构
[1] Kangwon Natl Univ, Dept Reg Infrastruct Engn, Chunchon 24341, South Korea
[2] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Pohang 37673, South Korea
[3] Smart Geotech, Chunchon 24341, South Korea
关键词
slope failure; machine learning; landslide; disaster; prediction; EARLY WARNING SYSTEMS; TIME-SERIES; MODELS;
D O I
10.3390/buildings13112691
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Slope failures pose significant threats to human safety and vital infrastructure. The urgent need for the accurate prediction of these geotechnical events is driven by two main goals: advancing our understanding of the underlying geophysical mechanisms and establishing efficient evacuation protocols. Although traditional physics-based models offer in-depth insights, their reliance on numerous assumptions and parameters limits their practical usability. In our study, we constructed an experimental artificial slope and monitored it until failure, generating an in-depth displacement dataset. Leveraging this dataset, we developed and compared prediction models rooted in both statistical and machine learning paradigms. Furthermore, to bridge the gap between generic evaluation metrics and the specific needs of slope failure prediction, we introduced a bespoke performance. Our results indicate that while the statistical approach did not effectively provide early warnings, the machine learning models, when assessed with our bespoke performance metric, showed significant promise as reliable early warning systems. These findings hold potential to fortify disaster prevention measures and prioritize human safety.
引用
收藏
页数:19
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