36 citations as recorded by crossref.

1. Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks L. Lucchese et al. https://doi.org/10.1016/j.catena.2020.105067
2. Integration of artificial neural network and geographical information system for intelligent assessment of land suitability for the cultivation of a selected crop F. Farnood Ahmadi & N. Farsad Layegh https://doi.org/10.1007/s00521-014-1801-z
3. Rapid Assessment of the Co-seismic Landslide Hazard Triggered by the 2022 Ms 6.8 Luding Earthquake G. Du et al. https://doi.org/10.1007/s12583-023-1846-y
4. Landslide Susceptibility Mapping in North Tehran, Iran: Linear Regression, Neural Networks, and Fuzzy Logic Approaches A. Ghaedi Vanani et al. https://doi.org/10.1007/s10706-024-02920-3
5. Assessment of landslides induced by earthquake risk of Istanbul: A comprehensive study utilizing an integrated DFS-AHP and DFS-EDAS approach B. Yalcin Kavus & A. Taskin https://doi.org/10.1016/j.soildyn.2025.109285
6. Natural Laboratories for Field Observation About Genesis and Landscape Effects of Palaeo-Earthquakes: a Proposal for the Rocca Busambra and Monte Barracù Geosites (West Sicily) L. Basilone et al. https://doi.org/10.1007/s12371-018-0334-8
7. Remote sensing GIS-based landslide susceptibility & risk modeling in Darjeeling–Sikkim Himalaya together with FEM-based slope stability analysis of the terrain S. Nath et al. https://doi.org/10.1007/s11069-021-04823-5
8. Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java G. Samodra et al. https://doi.org/10.1007/s12665-017-6475-2
9. Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping H. Hong et al. https://doi.org/10.1016/j.scitotenv.2020.140549
10. Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM J. Dou et al. https://doi.org/10.3390/rs11060638
11. Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies E. Rotigliano et al. https://doi.org/10.1007/s12665-019-8214-3
12. Optimizing slope unit-based landslide susceptibility mapping using the priority-flood flow direction algorithm G. Yan et al. https://doi.org/10.1016/j.catena.2023.107657
13. Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments W. Chen et al. https://doi.org/10.3390/rs12233854
14. Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach A. Saha & S. Saha https://doi.org/10.1007/s10668-020-00783-1
15. Incorporating Triggering and Environmental Factors in the Analysis of Earthquake-Induced Landslide Hazards M. Wang et al. https://doi.org/10.1007/s13753-014-0020-7
16. Investigating Limits in Exploiting Assembled Landslide Inventories for Calibrating Regional Susceptibility Models: A Test in Volcanic Areas of El Salvador C. Martinello et al. https://doi.org/10.3390/app12126151
17. Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India J. Barman et al. https://doi.org/10.1016/j.nhres.2023.06.006
18. Shear wave velocity and soil type microzonation using neural networks and geographic information system M. Motalleb Nejad et al. https://doi.org/10.1016/j.soildyn.2017.10.001
19. Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment L. Lucchese et al. https://doi.org/10.1007/s10661-019-7968-0
20. A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece) C. Polykretis et al. https://doi.org/10.1007/s10064-014-0607-7
21. A three-stage and multi-objective stochastic programming model to improve the sustainable rescue ability by considering secondary disasters in emergency logistics J. Zhang et al. https://doi.org/10.1016/j.cie.2019.02.003
22. ESI-07 ShakeMaps for instrumental and historical events in the Betic Cordillera (SE Spain): An approach based on geological data and applied to seismic hazard P. Silva et al. https://doi.org/10.1016/j.quaint.2016.10.020
23. Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan J. Li et al. https://doi.org/10.3390/w13223312
24. Landslide vulnerability mapping using GIS-based statistical model for sustainable ecosystem management in the Himalayan region of Teesta River basin, India S. Pal et al. https://doi.org/10.1186/s12302-025-01107-8
25. Earthquake-induced landslide scenarios for seismic microzonation: application to the Accumoli area (Rieti, Italy) S. Martino et al. https://doi.org/10.1007/s10518-019-00589-1
26. A review of recent studies on landslide hazard in Latin America A. Valdés Carrera et al. https://doi.org/10.1080/02723646.2021.1978372
27. A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment Q. Ba et al. https://doi.org/10.1007/s12145-018-0335-9
28. GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria F. Mahdadi et al. https://doi.org/10.1007/s12517-018-3770-5
29. Macrobenthos habitat potential mapping using GIS-based artificial neural network models S. Lee et al. https://doi.org/10.1016/j.marpolbul.2012.10.023
30. Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal B. Pokharel et al. https://doi.org/10.1007/s10346-020-01558-5
31. Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms A. Can et al. https://doi.org/10.1007/s10064-017-1034-3
32. Earthquake-induced landslides susceptibility assessment: A review of the state-of-the-art X. Shao & C. Xu https://doi.org/10.1016/j.nhres.2022.03.002
33. A two-stage stochastic optimization for disaster rescue resource distribution considering multiple disasters B. Wang et al. https://doi.org/10.1080/0305215X.2022.2144277
34. Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility H. Wang et al. https://doi.org/10.1186/s40677-017-0076-y
35. Multiobjective Emergency Resource Allocation under the Natural Disaster Chain with Path Planning F. Wang et al. https://doi.org/10.3390/ijerph19137876
36. Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks D. Tien Bui et al. https://doi.org/10.1016/j.geomorph.2012.04.023