Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1481-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-1481-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Apr 2025
Research article |
| 24 Apr 2025
| 24 Apr 2025
Prediction of the volume of shallow landslides due to rainfall using data-driven models
Jérémie Tuganishuri
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Chan-Young Yune
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Gihong Kim
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Seung Woo Lee
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Manik Das Adhikari
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
Sang-Guk Yum
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Gangneung–Wonju National University, Gangneung, Gangwon 25457, South Korea
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Short summary
To reduce the consequences of landslides due to rainfall, such as loss of life, economic losses, and disruption to daily living, this study describes the process of building a machine learning model which can help to estimate the volume of landslide material that can occur in a particular region, taking into account antecedent rainfall, soil characteristics, type of vegetation, etc. The findings can be useful for land use management, infrastructure design, and rainfall disaster management.
To reduce the consequences of landslides due to rainfall, such as loss of life, economic losses,...
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