| 1 | A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran | Science of the Total Environment | 2019 | 257 |
| 2 | Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran | Remote Sensing | 2020 | 209 |
| 3 | GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches | Science of the Total Environment | 2019 | 202 |
| 4 | Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models | Science of the Total Environment | 2018 | 187 |
| 5 | A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India | Remote Sensing | 2019 | 168 |
| 6 | Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India | Science of the Total Environment | 2021 | 163 |
| 7 | Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques | Journal of Hydrology | 2020 | 153 |
| 8 | Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS | Journal of Environmental Management | 2019 | 149 |
| 9 | Comparison of machine learning models for gully erosion susceptibility mapping | Geoscience Frontiers | 2020 | 143 |
| 10 | Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs | Remote Sensing | 2019 | 137 |
| 11 | Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms | Water (Switzerland) | 2021 | 125 |
| 12 | GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms | Journal of Mountain Science | 2019 | 124 |
| 13 | Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms | Applied Sciences (Switzerland) | 2018 | 113 |
| 14 | Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm | Land Degradation and Development | 2018 | 111 |
| 15 | Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility | Science of the Total Environment | 2019 | 111 |
| 16 | Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques | Catena | 2019 | 110 |
| 17 | Decision tree based ensemble machine learning approaches for landslide susceptibility mapping | Geocarto International | 2022 | 105 |
| 18 | Geomatics, Natural Hazards and Risk | Geomatics, Natural Hazards and Risk | 2018 | 101 |
| 19 | Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India | Sensors | 2020 | 100 |
| 20 | Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India | Science of the Total Environment | 2020 | 99 |
| 21 | Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method | Science of the Total Environment | 2021 | 98 |
| 22 | Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms | Geomatics, Natural Hazards and Risk | 2021 | 96 |
| 23 | Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India | Environmental Earth Sciences | 2020 | 95 |
| 24 | A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility | Science of the Total Environment | 2020 | 93 |
| 25 | Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model | Remote Sensing | 2020 | 90 |
| 26 | Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling | Catena | 2019 | 87 |
| 27 | Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran) | Remote Sensing | 2020 | 83 |
| 28 | Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility | Remote Sensing | 2020 | 81 |
| 29 | Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment | Remote Sensing | 2020 | 77 |
| 30 | Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility | Remote Sensing | 2020 | 75 |
| 31 | Flash-flood hazard using deep learning based on HO R package and fuzzy-multicriteria decision-making analysis | Journal of Hydrology | 2022 | 75 |
| 32 | Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India | Geocarto International | 2022 | 72 |
| 33 | Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques | Journal of Hydrology: Regional Studies | 2021 | 72 |
| 34 | Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran | Remote Sensing | 2019 | 67 |
| 35 | Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models | Stochastic Environmental Research and Risk Assessment | 2022 | 67 |
| 36 | New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping | Hydrological Sciences Journal | 2020 | 66 |
| 37 | Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran | Remote Sensing | 2020 | 65 |
| 38 | Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements | Journal of Environmental Management | 2021 | 63 |
| 39 | Detection of areas prone to flood risk using state-of-the-art machine learning models | Geomatics, Natural Hazards and Risk | 2021 | 63 |
| 40 | Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors | Sensors | 2021 | 63 |
| 41 | Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence | Water (Switzerland) | 2019 | 61 |
| 42 | An ensemble model for landslide susceptibility mapping in a forested area | Geocarto International | 2020 | 60 |
| 43 | Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility | Remote Sensing | 2020 | 60 |
| 44 | Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility | Remote Sensing | 2020 | 58 |
| 45 | Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran) | Remote Sensing | 2019 | 57 |
| 46 | Applying different scenarios for landslide spatial modeling using computational intelligence methods | Environmental Earth Sciences | 2017 | 55 |
| 47 | Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach | Frontiers in Earth Science | 2021 | 54 |
| 48 | Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility | Remote Sensing | 2020 | 53 |
| 49 | Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation | Journal of Environmental Management | 2021 | 53 |
| 50 | Drought risk assessment: integrating meteorological, hydrological, agricultural and socio-economic factors using ensemble models and geospatial techniques | Geocarto International | 2022 | 51 |