44 citations as recorded by crossref.

1. Integrating Expert Knowledge with Statistical Analysis for Landslide Susceptibility Assessment at Regional Scale C. Chalkias et al. https://doi.org/10.3390/geosciences6010014
2. Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran K. Shirani et al. https://doi.org/10.1007/s11069-018-3356-2
3. Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression C. Chalkias et al. https://doi.org/10.1007/s10064-020-01733-x
4. Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China Q. Wang et al. https://doi.org/10.3390/rs9090938
5. Landslide susceptibility zonation using geospatial technique and analytical hierarchy process in Sikkim Himalaya I. Sonker et al. https://doi.org/10.1016/j.qsa.2021.100039
6. Landslides and Cultural Heritage—A Review J. Bonini et al. https://doi.org/10.3390/heritage6100348
7. GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia Z. Anis et al. https://doi.org/10.1515/geo-2019-0056
8. Assessing landslide susceptibility and dynamics at cultural heritage sites by integrating machine learning techniques and persistent scatterer interferometry J. Bonini et al. https://doi.org/10.1016/j.geomorph.2024.109522
9. The artificial neural network for the rockfall susceptibility assessment. A case study in Basilicata (Southern Italy) L. Losasso & F. Sdao https://doi.org/10.1080/19475705.2018.1476413
10. Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices L. Tan et al. https://doi.org/10.1007/s11069-020-04429-3
11. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models H. Hong et al. https://doi.org/10.1016/j.geomorph.2016.02.012
12. A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN) H. Al-Najjar et al. https://doi.org/10.3390/rs13194011
13. Application of analytical hierarchal process, frequency ratio, and Shannon entropy approaches for landslide susceptibility mapping using geospatial technology: The case of Dejen district, Ethiopia T. Melese et al. https://doi.org/10.1007/s12517-022-09672-5
14. Risk assessment and its influencing factors analysis of geological hazards in typical mountain environment J. Lin et al. https://doi.org/10.1016/j.jclepro.2021.127077
15. An objective absence data sampling method for landslide susceptibility mapping Y. Rabby et al. https://doi.org/10.1038/s41598-023-28991-5
16. GIS-based landslide spatial modeling in Ganzhou City, China H. Hong et al. https://doi.org/10.1007/s12517-015-2094-y
17. A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India T. Basu & S. Pal https://doi.org/10.1007/s10668-019-00406-4
18. Identification of the significant parameters in spatial prediction of landslide hazard A. Tyagi et al. https://doi.org/10.1007/s10064-023-03334-w
19. Natural risk assessment and mitigation of cultural heritage sites in North‐eastern Romania (Valea Oii river basin) I. Nicu https://doi.org/10.1111/area.12433
20. Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area C. Polykretis et al. https://doi.org/10.1007/s10064-017-1125-1
21. Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal P. Gautam et al. https://doi.org/10.1186/s40677-021-00200-3
22. Application of novel hybrid model for land subsidence susceptibility mapping Z. Shen et al. https://doi.org/10.1002/gj.4603
23. Artificial neural network and sensitivity analysis in the landslide susceptibility mapping of Idukki district, India J. Jacinth Jennifer & S. Saravanan https://doi.org/10.1080/10106049.2021.1923831
24. Hierarchical Classification of Groundwater Pollution Risk of Contaminated Sites Using Fuzzy Logic: A Case Study in the Basilicata Region (Italy) D. Caniani et al. https://doi.org/10.3390/w7052013
25. GIS-based evaluation of diagnostic areas in landslide susceptibility analysis of Bahluieț River Basin (Moldavian Plateau, NE Romania). Are Neolithic sites in danger? I. Nicu & A. Asăndulesei https://doi.org/10.1016/j.geomorph.2018.04.010
26. Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China W. Wang et al. https://doi.org/10.1007/s11069-020-04128-z
27. Application of AHP based geospatial modeling for assessment of landslide hazard zonation along Mughal road in the Pir Panjal Himalayas I. Bhat et al. https://doi.org/10.1007/s12665-023-10952-w
28. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. https://doi.org/10.3390/rs16234491
29. Landslide risk assessment using a multi-method approach in Hashtchin region (NW of Iran) R. Talaei https://doi.org/10.1007/s40328-014-0052-x
30. Human health risk assessment of fluoride-rich groundwater using fuzzy-analytical process over the conventional technique V. Laxmi Mohanta et al. https://doi.org/10.1016/j.gsd.2019.100291
31. Exploring the Impact of Analysis Scale on Landslide Susceptibility Modeling: Empirical Assessment in Northern Peloponnese, Greece C. Polykretis et al. https://doi.org/10.3390/geosciences8070261
32. A risk assessment proposal for underground cavities in Hard Soils-Soft Rocks Z. Margherita et al. https://doi.org/10.1016/j.ijrmms.2018.01.024
33. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models C. Polykretis & C. Chalkias https://doi.org/10.1007/s11069-018-3299-7
34. Habitat ecological integrity and environmental impact assessment of anthropic activities: A GIS-based fuzzy logic model for sites of high biodiversity conservation interest D. Caniani et al. https://doi.org/10.1016/j.ecolind.2016.02.038
35. A geospatial information quantity model for regional landslide risk assessment Y. Tan et al. https://doi.org/10.1007/s11069-015-1909-1
36. Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India N. Dam et al. https://doi.org/10.1155/2022/6645007
37. Mapping of landslide susceptibility using the combination of neuro-fuzzy inference system (ANFIS), ant colony (ANFIS-ACOR), and differential evolution (ANFIS-DE) models S. Razavi-Termeh et al. https://doi.org/10.1007/s10064-020-02048-7
38. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size P. Tsangaratos & I. Ilia https://doi.org/10.1016/j.catena.2016.06.004
39. Unfreezing the past: near Pan-Svalbard assessment of cryospheric hazards to Arctic cultural heritage I. Nicu et al. https://doi.org/10.1016/j.scitotenv.2025.180424
40. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy) M. Conforti et al. https://doi.org/10.1016/j.catena.2013.08.006
41. Feature elimination and comparison of machine learning algorithms in landslide susceptibility mapping J. Jennifer https://doi.org/10.1007/s12665-022-10620-5
42. Applying Close Range Non-Destructive Techniques for the Detection of Conservation Problems in Rock-Carved Cultural Heritage Sites W. Frodella et al. https://doi.org/10.3390/rs13051040
43. Quantitative landslide risk analysis in the Hashtchin area (NW-Iran) R. Talaei & S. Samadov https://doi.org/10.1080/19648189.2016.1229224
44. Bagging based Support Vector Machines for spatial prediction of landslides B. Pham et al. https://doi.org/10.1007/s12665-018-7268-y