16 citations as recorded by crossref.

1. Landslide susceptibility analysis for Ancient Shu Roads using ensemble machine learning H. Feng et al. https://doi.org/10.1038/s40494-026-02659-5
2. Statistical–machine learning coupled models predicting landslide susceptibility in Liangshan Yi Autonomous Prefecture: performance comparison and factor explanations Z. Ning et al. https://doi.org/10.1007/s10064-026-04917-z
3. Evaluating the factors affecting landslides using machine learning algorithms (case study: the catchment area of Karun-3 Dam, Iran) R. Zandi & G. Shah Pari Far https://doi.org/10.1016/j.ejrs.2025.07.005
4. Spatial Assessment of Landslide Susceptibility for Sustainable Land-Use Planning in the Foothill Zones of the Almaty Agglomeration (Southeastern Kazakhstan) Using the Frequency Ratio Method and GIS A. Bekzhanova et al. https://doi.org/10.3390/su18179079
5. Engaging diverse stakeholders in exposure data collection: Participatory tools and activities C. Scaini et al. https://doi.org/10.1016/j.ijdrr.2025.105179
6. Quantitative landslide risk analysis with limited dataset by leveraging geospatial approaches in Indonesia D. Melati et al. https://doi.org/10.1007/s11069-025-07645-x
7. A hybrid framework for landslide prediction integrating machine learning and sample enhanced by spatial autocorrelation L. Ding et al. https://doi.org/10.1007/s00477-025-03079-w
8. Thickness inversion of large-scale active landslides: concepts, methods, and InSAR-based pathways G. Zhao et al. https://doi.org/10.1016/j.earscirev.2026.105728
9. A Geospatial Framework for Landslide Risk Assessment of Road Infrastructure at a Regional Level in Greece Z. Misiri et al. https://doi.org/10.3390/ijgi15060246
10. Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer J. Chou et al. https://doi.org/10.5194/nhess-25-119-2025
11. Quantitative landslide risk assessment for Italy F. Caleca et al. https://doi.org/10.1007/s10346-025-02590-z
12. Multi-Sensor Satellite Analysis for Landslide Characterization: A Case of Study from Baipaza, Tajikistan F. Poggi et al. https://doi.org/10.3390/rs17122003
13. Integrating multidisciplinary approach to decipher the peculier incidence of burst of new water source and perpetual problem of ground subsidence in Joshimath, Uttarakhand H. Bahuguna et al. https://doi.org/10.1007/s11069-025-07737-8
14. From landslide susceptibility to risk assessment in the conterminous United States F. Caleca et al. https://doi.org/10.1016/j.enggeo.2026.108994
15. Spatiotemporal assessment of landslide risk over large areas: a case study of the Valencian Community (1950–2021) I. Cantarino Martí et al. https://doi.org/10.5194/nhess-26-2505-2026
16. Residential community-based quantitative risk assessment of rainfall-induced loess landslides for the suburban residential areas of Chengguan district in Lanzhou city with an innovative framework J. He et al. https://doi.org/10.1016/j.enggeo.2026.108785