LANDSLIDE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS STUDY CASE AL HOCEIMA REGION, NORTHERN MOROCCO

6 citations as recorded by crossref.

1. Groundwater potential mapping in arid and semi-arid regions of kurdistan region of Iraq: A geoinformatics-based machine learning approach K. Fatah et al. 10.1016/j.gsd.2024.101337
2. Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region A. Sharma et al. 10.1007/s11629-024-8651-7
3. Influence of decomposed input variables on LSTM-based prediction of physico-chemical and biological parameters in Al Hoceima Bay (Moroccan Mediterranean sea) A. Hrida et al. 10.1007/s10236-025-01729-9
4. Exploring class imbalance with under-sampling, over-sampling, and hybrid sampling based on Mahalanobis distance for landslide susceptibility assessment: a case study of the 2018 Iburi earthquake induced landslides in Hokkaido, Japan K. Nam et al. 10.1007/s12303-023-0033-6
5. Machine learning techniques on spatio-temporal data for landslide susceptibility assessment at Dieng Mountainous Region, Banjarnegara district, Central Java, Indonesia Y. Susena et al. 10.1007/s11069-025-07136-z
6. Landslide risk using Geospatial techniques and machine learning: Shimla district of Himachal pradesh, India A. Sharma et al. 10.1007/s12665-025-12522-8