Abstract
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.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference104 articles.
1. Comparison of Landslide Susceptibility Mapping Methodologies for Koyulhisar, Turkey: Conditional Probability, Logistic Regression, Artificial Neural Networks, and Support Vector Machine;Environ. Earth Sci.,2010
2. Use of Satellite Remote Sensing Data in the Mapping of Global Landslide Susceptibility;Nat. Hazards,2007
3. Global Fatal Landslide Occurrence from 2004 to 2016;Nat. Hazards Earth Syst. Sci.,2018
4. Exploring the Effects of the Design and Quantity of Absence Data on the Performance of Random Forest-Based Landslide Susceptibility Mapping;Catena,2019
5. GIS-Based Landslide Susceptibility Mapping Using Analytical Hierarchy Process (AHP) and Certainty Factor (CF) Models for the Baozhong Region of Baoji City, China;Environ. Earth Sci.,2015
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