38 citations as recorded by crossref.

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4. A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management X. Sun et al. 10.1016/j.jenvman.2022.116108
5. Spatial agents for geological surface modelling E. de Kemp 10.5194/gmd-14-6661-2021
6. Landslide Risks to Bridges in Valleys in North Carolina S. Lin et al. 10.3390/geohazards5010015
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8. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization X. Zhou et al. 10.1016/j.gsf.2021.101211
9. Data use and data needs in critical infrastructure risk analysis A. Larsson & C. Große 10.1080/13669877.2023.2181858
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11. Recent advances in vulnerability assessment for the built environment exposed to torrential hazards: Challenges and the way forward S. Fuchs et al. 10.1016/j.jhydrol.2019.05.067
12. Towards a holistic assessment of landslide susceptibility models: insights from the Central Eastern Alps M. Schlögl et al. 10.1007/s12665-024-12041-y
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15. Total probabilistic measure for the potential risk of regional roads exposed to landslides Q. Liu et al. 10.1016/j.ress.2022.108822
16. Modelling landslides in the Lesser Himalaya region using geospatial and numerical simulation techniques M. Islam & S. Chattoraj 10.1007/s12517-023-11541-8
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20. Identification and evaluation of flood-avoidance routes in Tucson, Arizona A. Coles 10.1016/j.ijdrr.2020.101597
21. Natural hazard impacts on transport infrastructure in Russia E. Petrova 10.5194/nhess-20-1969-2020
22. Enhanced landslide susceptibility zonation using GIS-Based ensemble techniques A. Sharma et al. 10.1007/s12665-024-12032-z
23. Estimation of traffic flow changes using networks in networks approaches J. Hackl & B. Adey 10.1007/s41109-019-0139-y
24. Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning H. He et al. 10.3390/su16093828
25. The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. 10.1016/j.geomorph.2020.107041
26. Multi-hazard risk assessment of rail infrastructure in India under local vulnerabilities towards adaptive pathways for disaster resilient infrastructure planning D. Joshi et al. 10.1016/j.pdisas.2023.100308
27. Analysis of historical data for a better understanding of post‐construction landslides at an artificial waterway A. Wohlers & B. Damm 10.1002/esp.5028
28. Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms H. Shahabi et al. 10.3390/rs15123112
29. Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area B. Ghasemian et al. 10.3389/fenvs.2022.897254
30. Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis S. Yang et al. 10.1016/j.cageo.2024.105723
31. Rockfall Vulnerability of a Rural Road Network—A Methodological Approach in the Harz Mountains, Germany A. Wohlers & B. Damm 10.3390/geosciences12040170
32. Multi-hazard risk assessment for roads: probabilistic versus deterministic approaches S. Oberndorfer et al. 10.5194/nhess-20-3135-2020
33. Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors J. Verschuur et al. 10.1371/journal.pclm.0000331
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35. Rainfall-induced transportation embankment failure: A review X. Linrong et al. 10.1515/geo-2022-0558
36. A machine learning approach in spatial predicting of landslides and flash flood susceptible zones for a road network H. Ha et al. 10.1007/s40808-022-01384-9
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38. Quantitative assessment of the impact of earthquake-induced geohazards on natural landscapes in Jiuzhaigou Valley X. Hu et al. 10.1007/s11629-018-5240-7