Neural network derived landslide risk assessment system
DOI: https://doi.org/10.3846/jeelm.2026.28249Abstract
Landslide is a frequent form of natural disaster in a tropical region such as Western Sarawak, Malaysia, and the occurrences are commonly associated with the wet season. Contemporarily, Landslide Susceptibility Map (LSM) produced by machine learning models are used to assess the landslide risk. However, LSMs are known to have spatiotemporal issues, where recent changes in the spatial data used as inputs in the models are not reflected in the LSMs, causing unjustified landslide susceptibility score in certain areas. The same issue was also observed in Western Sarawak’s LSM. Thus, this study was focused on transforming the Artificial Neural Network model used developed the region’s LSM into a set of equations that can be used as a landslide risk assessment system. The new system was determined to be highly accurate with a score of 1 for both Area Under the Curve score, and recall.
Keywords:
environment impact assessment, environment monitoring, environment process modelling, landslide, machine learning, remote sensing.How to Cite
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Ministry of Higher Education, Malaysia
Grant numbers: FRGS/1/2022/TK06/UNIMAS/01/1
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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Funding data
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Ministry of Higher Education, Malaysia
Grant numbers: FRGS/1/2022/TK06/UNIMAS/01/1