| 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 | 483 | 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 | 216 | 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 | 207 | 2017 |
| Land cover classification from fused DSM and UAV images using convolutional neural networks HAH Al-Najjar, B Kalantar, B Pradhan, V Saeidi, AA Halin, N Ueda, ... Remote Sensing 11 (12), 1461, 2019 | 183 | 2019 |
| Groundwater potential mapping using a novel data-mining ensemble model MD Kordestani, SA Naghibi, H Hashemi, K Ahmadi, B Kalantar, ... Hydrogeology journal, 2019 | 169 | 2019 |
| Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data B Kalantar, N Ueda, V Saeidi, K Ahmadi, AA Halin, F Shabani Remote Sensing 12 (11), 1737, 2020 | 113 | 2020 |
| Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping H Nguyen, M Mehrabi, B Kalantar, H Moayedi Geomatics, Natural Hazards and Risk, 2019 | 107 | 2019 |
| Forest fire susceptibility prediction based on machine learning models with resampling algorithms on remote sensing data B Kalantar, N Ueda, MO Idrees, S Janizadeh, K Ahmadi, F Shabani Remote Sensing 12 (22), 3682, 2020 | 99 | 2020 |
| Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia HJ Jumaah, MH Ameen, B Kalantar, HM Rizeei, SJ Jumaah Geomatics, Natural Hazards and Risk 10 (1), 2185-2199, 2019 | 99 | 2019 |
| A novel swarm intelligence—Harris hawks optimization for spatial assessment of landslide susceptibility DT Bui, H Moayedi, B Kalantar, A Osouli, B Pradhan, H Nguyen, ... Sensors 19 (16), 3590, 2019 | 96 | 2019 |
| Fire‐Net: A Deep Learning Framework for Active Forest Fire Detection ST Seydi, V Saeidi, B Kalantar, N Ueda, AA Halin Journal of Sensors 2022 (1), 8044390, 2022 | 93 | 2022 |
| Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide H Moayedi, M Mehrabi, B Kalantar, M Abdullahi Mu’azu, AS A. Rashid, ... Geomatics, Natural Hazards and Risk 10 (1), 1879-1911, 2019 | 87 | 2019 |
| GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran SM Mousavi, A Golkarian, SA Naghibi, B Kalantar, B Pradhan Aims Geosci 3 (1), 91-115, 2017 | 84 | 2017 |
| Invasive weed species’ threats to global biodiversity: Future scenarios of changes in the number of invasive species in a changing climate F Shabani, M Ahmadi, L Kumar, S Solhjouy-fard, MS Tehrany, F Shabani, ... Ecological Indicators 116, 106436, 2020 | 79 | 2020 |
| Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis B Kalantar, SB Mansor, MI Sameen, B Pradhan, HZM Shafri International journal of remote sensing 38 (8-10), 2535-2556, 2017 | 78 | 2017 |
| Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential SA Naghibi, M Dolatkordestani, A Rezaei, P Amouzegari, MT Heravi, ... Environmental monitoring and assessment 191, 1-20, 2019 | 77 | 2019 |
| Optimized conditioning factors using machine learning techniques for groundwater potential mapping B Kalantar, HAH Al-Najjar, B Pradhan, V Saeidi, AA Halin, N Ueda, ... Water 11 (9), 1909, 2019 | 76 | 2019 |
| Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach H Hong, B Pradhan, MI Sameen, B Kalantar, A Zhu, W Chen Landslides 15, 753-772, 2018 | 73 | 2018 |
| Oil palm counting and age estimation from WorldView‐3 imagery and LiDAR data using an integrated OBIA height model and regression analysis HM Rizeei, HZM Shafri, MA Mohamoud, B Pradhan, B Kalantar Journal of Sensors 2018 (1), 2536327, 2018 | 71 | 2018 |
| Deep neural network utilizing remote sensing datasets for flood hazard susceptibility mapping in Brisbane, Australia B Kalantar, N Ueda, V Saeidi, S Janizadeh, F Shabani, K Ahmadi, ... Remote Sensing 13 (13), 2638, 2021 | 61 | 2021 |
