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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2024-86', Anonymous Referee #1, 09 Jul 2024
    • AC1: 'Reply on RC1', Jui-Sheng Chou, 21 Aug 2024
  • RC2: 'Comment on nhess-2024-86', Anonymous Referee #2, 01 Aug 2024
    • AC2: 'Reply on RC2', Jui-Sheng Chou, 21 Aug 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (29 Aug 2024) by Paola Reichenbach
AR by Jui-Sheng Chou on behalf of the Authors (29 Aug 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Sep 2024) by Paola Reichenbach
RR by Anonymous Referee #2 (10 Oct 2024)
RR by Anonymous Referee #1 (11 Oct 2024)
ED: Publish subject to technical corrections (11 Oct 2024) by Paola Reichenbach
AR by Jui-Sheng Chou on behalf of the Authors (21 Oct 2024)  Author's response   Manuscript 
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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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