Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-119-2025
https://doi.org/10.5194/nhess-25-119-2025
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

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Latest update: 06 Jun 2025
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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.
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