COMPARING MODEL PERFORMANCE METRICS FOR LANDSLIDE SUSCEPTIBILITY MAPPING

10 citations as recorded by crossref.

1. Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy V. Yordanov & M. Brovelli https://doi.org/10.1007/s12518-020-00344-1
2. Understanding the scale effects of topographical variables on landslide susceptibility mapping in Sikkim Himalaya using deep learning approaches A. Saha et al. https://doi.org/10.1080/10106049.2022.2136255
3. The Spatial-temporal Evolution Evaluation of Landslide Susceptibility in the 15 Years After the Earthquake (A Case Study of Shifang City) Q. Yongping & H. Jianhua https://doi.org/10.1007/s41742-026-01093-6
4. Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine M. Brovelli et al. https://doi.org/10.3390/ijgi9100580
5. Landslide Risk Associated with Glacier Tourism in the Mt. Everest Region (Sagarmatha National Park), High-Mountain Nepal L. Sapkota et al. https://doi.org/10.3390/earth7020043
6. Assessing flood susceptibility and effectiveness of structural flood mitigation measures applied within Mubuku catchment in Rwenzori Region, Uganda S. Nedala et al. https://doi.org/10.1007/s11069-024-06843-3
7. Shaping shallow landslide susceptibility as a function of rainfall events M. Fumagalli et al. https://doi.org/10.5194/nhess-25-4405-2025
8. Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks H. Zhang et al. https://doi.org/10.1007/s11069-022-05748-3
9. A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence H. Fang et al. https://doi.org/10.3390/su15043094
10. Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy Q. Xu et al. https://doi.org/10.1080/17538947.2024.2346263