| GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran SA Naghibi, HR Pourghasemi, B Dixon Environmental monitoring and assessment 188, 1-27, 2016 | 695 | 2016 |
| Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and … B Kalantar, B Pradhan, SA Naghibi, A Motevalli, S Mansor Geomatics, Natural Hazards and Risk 9 (1), 49-69, 2018 | 511 | 2018 |
| Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping SA Naghibi, K Ahmadi, A Daneshi Water Resources Management 31, 2761-2775, 2017 | 421 | 2017 |
| Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran SA Naghibi, HR Pourghasemi, ZS Pourtaghi, A Rezaei Earth Science Informatics 8, 171-186, 2015 | 367 | 2015 |
| A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping SA Naghibi, HR Pourghasemi Water resources management 29, 5217-5236, 2015 | 280 | 2015 |
| A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China W Chen, HR Pourghasemi, SA Naghibi Bulletin of Engineering Geology and the Environment 77, 647-664, 2018 | 230 | 2018 |
| Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS A Golkarian, SA Naghibi, B Kalantar, B Pradhan Environmental monitoring and assessment 190, 1-16, 2018 | 225 | 2018 |
| A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping SA Naghibi, DD Moghaddam, B Kalantar, B Pradhan, O Kisi Journal of Hydrology 548, 471-483, 2017 | 215 | 2017 |
| Groundwater potential mapping using a novel data-mining ensemble model MD Kordestani, SA Naghibi, H Hashemi, K Ahmadi, B Kalantar, ... Hydrogeology journal, 2019 | 176 | 2019 |
| A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS SA Naghibi, HR Pourghasemi, K Abbaspour Theoretical and applied climatology 131, 967-984, 2018 | 174 | 2018 |
| Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches O Rahmati, SA Naghibi, H Shahabi, DT Bui, B Pradhan, A Azareh, ... Journal of hydrology 565, 248-261, 2018 | 167 | 2018 |
| GIS-based landslide spatial modeling in Ganzhou City, China H Hong, SA Naghibi, HR Pourghasemi, B Pradhan Arabian Journal of Geosciences 9, 1-26, 2016 | 162 | 2016 |
| Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia O Rahmati, F Falah, KS Dayal, RC Deo, F Mohammadi, T Biggs, ... Science of the total environment 699, 134230, 2020 | 144 | 2020 |
| Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities O Rahmati, A Golkarian, T Biggs, S Keesstra, F Mohammadi, ... Journal of Environmental Management 236, 466-480, 2019 | 139 | 2019 |
| Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms W Chen, HR Pourghasemi, SA Naghibi Bulletin of Engineering Geology and the Environment 77, 611-629, 2018 | 138 | 2018 |
| Land subsidence modelling using tree-based machine learning algorithms O Rahmati, F Falah, SA Naghibi, T Biggs, M Soltani, RC Deo, A Cerdà, ... Science of the total environment 672, 239-252, 2019 | 136 | 2019 |
| A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping … H Hong, SA Naghibi, M Moradi Dashtpagerdi, HR Pourghasemi, W Chen Arabian Journal of Geosciences 10, 1-14, 2017 | 127 | 2017 |
| Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features SA Naghibi, MM Dashtpagerdi Hydrogeology journal 25 (1), 169, 2017 | 124 | 2017 |
| Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors SA Naghibi, H Hashemi, R Berndtsson, S Lee Journal of Hydrology 589, 125197, 2020 | 109 | 2020 |
| Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater A Motevalli, SA Naghibi, H Hashemi, R Berndtsson, B Pradhan, ... Journal of cleaner production 228, 1248-1263, 2019 | 108 | 2019 |
