Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 1
Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-119-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-119-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 1
Research article
 | 
06 Jan 2025
Research article |  | 06 Jan 2025

Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer

Jui-Sheng Chou, Hoang-Minh Nguyen, Huy-Phuong Phan, and Kuo-Lung Wang

Viewed

Total article views: 1,649 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,186 249 214 1,649 45 48
  • HTML: 1,186
  • PDF: 249
  • XML: 214
  • Total: 1,649
  • BibTeX: 45
  • EndNote: 48
Views and downloads (calculated since 10 Jun 2024)
Cumulative views and downloads (calculated since 10 Jun 2024)

Viewed (geographical distribution)

Total article views: 1,649 (including HTML, PDF, and XML) Thereof 1,577 with geography defined and 72 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Jun 2025
Download
Short summary
This study enhances landslide prediction using advanced machine learning, including new algorithms inspired by historical explorations. The research accurately forecasts landslide movements by analyzing 8 years of data from Taiwan's Lushan, improving early warning and potentially saving lives and infrastructure. This integration marks a significant advancement in environmental risk management.
Read more
Share
Altmetrics
Final-revised paper
Preprint