34 citations as recorded by crossref.

1. Utilizing Remote Sensing and Random Forests to Identify Optimal Land Use Scenarios and Address the Increase in Landslide Susceptibility A. Putra et al. https://doi.org/10.3390/su17094227
2. A quantitative framework to assess the debris flow hazard at a catchment scale under climate change Z. Guo et al. https://doi.org/10.1002/esp.70278
3. VIGIRISKS platform, a web-tool for single and multi-hazard risk assessment C. Negulescu et al. https://doi.org/10.1007/s11069-022-05567-6
4. Landslide Susceptibility Mapping Using Machine Learning: A Danish Case Study A. Ageenko et al. https://doi.org/10.3390/ijgi11060324
5. Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes Q. Pham et al. https://doi.org/10.1080/10106049.2021.1986579
6. Measurement and inversion strategies for 3‐D resistivity surveys with vector arrays M. Loke et al. https://doi.org/10.1111/1365-2478.13177
7. Prolonged influence of urbanization on landslide susceptibility T. Rohan et al. https://doi.org/10.1007/s10346-023-02050-6
8. Dynamics of population exposure to landslides under climate change scenarios P. Ghosh et al. https://doi.org/10.1016/j.ijdrr.2026.106041
9. Influence of Carbonate-Flysch Contact and Groundwater Dynamics on the Occurrence of Geohazards in Istria, Croatia S. Dugonjić Jovančević et al. https://doi.org/10.3390/land10050441
10. Landslide susceptibility in Xiji county: a coupled modeling approach with dynamic factors Q. Ling et al. https://doi.org/10.1007/s10064-025-04620-5
11. Implications of future land-use/cover pattern change on landslide susceptibility at a national level: A scenario-based analysis in Romania M. Jurchescu et al. https://doi.org/10.1016/j.catena.2023.107330
12. SCoMHA: a framework to analyse process-level sediment connectivity in multi-hazard events M. Ishmam et al. https://doi.org/10.1007/s11069-026-08149-y
13. Assessing the impact of climate change on landslide recurrence intervals in Nakhon Si Thammarat Province, Thailand, using CMIP6 climate models T. Chaithong https://doi.org/10.1016/j.pdisas.2024.100330
14. A probabilistic model for natural gas pipeline failure under climate-induced Natech hazards: Toward AI-based safety management G. Qin et al. https://doi.org/10.1016/j.jlp.2025.105849
15. Current understanding and uncertainties associated with climate change and the impact on slope stability: A systematic literature review F. Tetteh et al. https://doi.org/10.1016/j.nhres.2025.01.011
16. Energy and structural evolution process of high-altitude and long-runout landslides induced by a strong earthquake Y. Ge et al. https://doi.org/10.5194/nhess-26-1955-2026
17. Climate change increases the number of landslides at the juncture of the Alpine, Pannonian and Mediterranean regions M. Jemec Auflič et al. https://doi.org/10.1038/s41598-023-50314-x
18. How will the projected climate change influence rainfall-induced landslides in Europe? A review of modelling approaches S. Gariano & G. Rianna https://doi.org/10.1007/s10346-025-02550-7
19. Artificial Intelligence: A new era for spatial modelling and interpreting climate-induced hazard assessment A. Dikshit et al. https://doi.org/10.1016/j.gsf.2024.101815
20. Physically based model for assessing rainfall-induced deep-seated landslides using a hydrological-geotechnical model S. Phakdimek et al. https://doi.org/10.1080/19475683.2025.2481063
21. Displacement prediction of slow-moving landslides using InSAR and ensemble regression models based on slope units S. Cobos-Mora et al. https://doi.org/10.1007/s11069-025-07829-5
22. Impacts of future climate and land cover changes on landslide susceptibility: regional scale modelling in the Val d’Aran region (Pyrenees, Spain) M. Hürlimann et al. https://doi.org/10.1007/s10346-021-01775-6
23. Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets M. Conforti et al. https://doi.org/10.1007/s12665-023-10844-z
24. Improving the spatial prediction of machine learning-based landslide susceptibility models by integrating the particle swarm optimization algorithm R. Ajin et al. https://doi.org/10.1007/s00477-025-03101-1
25. Integrated method for fragility assessment of railway embankments under rainfall-induced landslides U. Sathyaras et al. https://doi.org/10.1007/s11069-026-08160-3
26. The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan N. Lusiana & Y. Shinohara https://doi.org/10.3390/w14132113
27. Determining the impact of anthropogenic activities and climate change on landslide susceptibility for the Himalayan region A. Tyagi et al. https://doi.org/10.1007/s11069-024-07011-3
28. Land Use/ Land Cover Changes and Climate Change as Conditioning Factors for Landslide: A Review R. Verma et al. https://doi.org/10.9719/EEG.2026.59.1.159
29. A novel hybrid model integrating high resolution remote sensing and stacking ensemble techniques for landslide susceptibility mapping: Application to event-based landslide inventory Z. Guo et al. https://doi.org/10.1016/j.geomorph.2025.109886
30. Prediction of the future landslide susceptibility scenario based on LULC and climate projections A. Tyagi et al. https://doi.org/10.1007/s10346-023-02088-6
31. Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China Z. Guo et al. https://doi.org/10.1016/j.gsf.2023.101542
32. Spatial understanding of historical and future landslide variation in Africa L. Nahayo et al. https://doi.org/10.1007/s11069-023-06126-3
33. Integrating dynamic factors for predicting future landslide susceptibility S. Lamichhane et al. https://doi.org/10.1007/s12665-025-12094-7
34. Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China H. Zheng & M. Ding https://doi.org/10.1016/j.ecoinf.2024.102862