Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 4
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.
https://doi.org/10.5194/nhess-25-1481-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 4
Research article
 | 
24 Apr 2025
Research article |  | 24 Apr 2025

Prediction of the volume of shallow landslides due to rainfall using data-driven models

Jérémie Tuganishuri, Chan-Young Yune, Gihong Kim, Seung Woo Lee, Manik Das Adhikari, and Sang-Guk Yum

Viewed

Total article views: 690 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
452 162 76 690 25 33
  • HTML: 452
  • PDF: 162
  • XML: 76
  • Total: 690
  • BibTeX: 25
  • EndNote: 33
Views and downloads (calculated since 11 Jul 2024)
Cumulative views and downloads (calculated since 11 Jul 2024)

Viewed (geographical distribution)

Total article views: 690 (including HTML, PDF, and XML) Thereof 657 with geography defined and 33 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Apr 2025
Download
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.

Read more
Share
Altmetrics
Final-revised paper
Preprint