| 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), 44, 2016 | 633 | 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 | 455 | 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 | 385 | 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 | 336 | 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 (14), 5217-5236, 2015 | 262 | 2015 |
| 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 | 209 | 2018 |
| 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, 1-18, 2017 | 208 | 2017 |
| 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 | 198 | 2017 |
| 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 | 162 | 2018 |
| Groundwater potential mapping using a novel data-mining ensemble model MD Kordestani, SA Naghibi, H Hashemi, K Ahmadi, B Kalantar, ... Hydrogeology journal, 2019 | 158 | 2019 |
| 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 | 149 | 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 | 145 | 2016 |
| 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, 1-19, 2017 | 133 | 2017 |
| 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 | 123 | 2020 |
| 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 | 115 | 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 | 114 | 2017 |
| Land subsidence modelling using tree-based machine learning algorithms O Rahmati, F Falah, SA Naghibi, T Biggs, M Soltani, RC Deo, A Cerdą, ... Science of the total environment 672, 239-252, 2019 | 108 | 2019 |
| 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, 2019 | 95 | 2019 |
| 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 | 91 | 2020 |
| A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping M Avand, S Janizadeh, SA Naghibi, HR Pourghasemi, ... Water 11 (10), 2076, 2019 | 91 | 2019 |
Hossein HashemiAssociate Prof. Lund UniversityVerified email at tvrl.lth.se
