Journal
REMOTE SENSING
Volume 14, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/rs14225795
Keywords
dynamic landslide susceptibility; machine learning; rainfall; global scale
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Funding
- National Natural Science Foundation of China [41771538]
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Precipitation is the main factor that triggers landslides. Developing a global dynamic rainfall-induced landslide susceptibility model is crucial for revealing landslide mechanisms, providing accurate landslide susceptibility maps for risk assessment and hazard prediction.
Precipitation is the main factor that triggers landslides. Rainfall-induced landslide susceptibility mapping (LSM) is crucial for disaster prevention and disaster losses mitigation, though most studies are temporally ambiguous and on a regional scale. To better reveal landslide mechanisms and provide more accurate landslide susceptibility maps for landslide risk assessment and hazard prediction, developing a global dynamic LSM model is essential. In this study, we used Google Earth Engine (GEE) as the main data platform and applied three tree-based ensemble machine learning algorithms to construct global, dynamic rainfall-induced LSM models based on dynamic and static landslide influencing factors. The dynamic perspective is used in LSM: dynamic changes in landslide susceptibility can be identified on a daily scale. We note that Random Forest algorithm offers robust performance for accurate LSM (AUC = 0.975) and although the classification accuracy of LightGBM is the highest (AUC = 0.977), the results do not meet the sufficient conditions of a landslide susceptibility map. Combined with quantitative precipitation products, the proposed model can be used for the release of historical and predictive global dynamic landslide susceptibility information.
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