Articles | Volume 26, issue 11
https://doi.org/10.5194/nhess-26-5469-2026
© Author(s) 2026. 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-26-5469-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
02 Nov 2026
Review article |
| 02 Nov 2026
| 02 Nov 2026
Review article: rainfall-induced landslide early warning systems through the lens of the early warnings for all framework – advances, gaps, and perspectives
Roquia Salam
CORRESPONDING AUTHOR
Department of Risk and Disaster Reduction (RDR), University College London (UCL), Gower Street, London, WC1E 6BT, UK
Bayes Ahmed
Department of Risk and Disaster Reduction (RDR), University College London (UCL), Gower Street, London, WC1E 6BT, UK
Peter Sammonds
Department of Risk and Disaster Reduction (RDR), University College London (UCL), Gower Street, London, WC1E 6BT, UK
Related authors
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Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singhc, Bayes Ahmed, Mahdi Motagh, Yang Wang, and Ling Chen
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-44, https://doi.org/10.5194/nhess-2023-44, 2023
Preprint withdrawn
Short summary
Short summary
We found that the altitude (< 240 m) and distance to rivers (< 300 m) emerged as important factors for the cause of landslides. LR-MLP-Boosting achieves the highest prediction accuracy. The coupling models outperform the corresponding single models and Boosting algorithm performs better than the Bagging algorithm. High-quality non-landslide samples enhance the accuracy of LSM. They can be effectively obtained by using the LR model to constrain its selection range.
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Short summary
Rainfall-triggered landslides threaten communities worldwide, yet many warning systems remain difficult to sustain or scale up. We reviewed 45 systems through the lens of the UN EW4All (United Nations ‘Early Warnings for All’) framework. We found 27 systems operational, but major gaps remained in risk mapping, data availability, community involvement, funding, and institutional capacity. Effective global warning coverage requires locally adapted, well-funded, institutionally capable, and people-centred approaches.
Rainfall-triggered landslides threaten communities worldwide, yet many warning systems remain...
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