| Flood prediction using machine learning models: Literature review A Mosavi, P Ozturk, K Chau | 1282 | 2018 |
| An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines B Choubin, E Moradi, M Golshan, J Adamowski, F Sajedi-Hosseini, ... Elsevier Science of the Total Environment, 2019 | 649 | 2019 |
| Sustainable business models: A review S Nosratabadi, A Mosavi, S Band, E Kazimieras Zavadskas, ... | 580 | 2019 |
| Covid-19 outbreak prediction with machine learning SF Ardabili, A Mosavi, P Ghamisi, F Ferdinand, AR Varkonyi-Koczy, ... | 550 | 2020 |
| State of the art of machine learning models in energy systems, a systematic review A Mosavi, M Salimi, S Faizollahzadeh Ardabili, T Rabczuk, ... | 500 | 2019 |
| Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis M Nabipour, P Nayyeri, H Jabani, S Shahab, A Mosavi IEEE 8, 150199-150212, 2020 | 379 | 2020 |
| Deep learning for stock market prediction M Nabipour, P Nayyeri, H Jabani, A Mosavi, E Salwana | 368 | 2020 |
| COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach G Pinter, I Felde, A Mosavi, P Ghamisi, R Gloaguen | 361 | 2020 |
| Predicting standardized streamflow index for hydrological drought using machine learning models S Band, S Hashemi, H Salimi, S Samadianfard, E Asadi, S Shadkani, ... Taylor & Francis 14 (1), 339-350, 2020 | 297* | 2020 |
| Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: application of the simulated annealing feature selection method FS Hoseini, B Choubin, A Mosavi, N Nabipour, S Band, H Darabi, ... Science of the total environment 711, 135161, 2020 | 294 | 2020 |
| Deep learning for detecting building defects using convolutional neural networks H Perez, JHM Tah, A Mosavi Sensors 19 (16), 3556, 2019 | 257 | 2019 |
| Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters S Band, E Jafari Nodoushan, JE Adolf, A Abdul Manaf, A Mosavi, K Chau Engineering Applications of Computational Fluid Mechanics 13 (1), 91-101, 2019 | 257 | 2019 |
| Integrated machine learning methods with resampling algorithms for flood susceptibility prediction E Dodangeh, B Choubin, AN Eigdir, N Nabipour, M Panahi, ... Science of the Total Environment 705, 135983, 2020 | 228 | 2020 |
| Advances in machine learning modeling reviewing hybrid and ensemble methods S Ardabili, A Mosavi, AR Várkonyi-Kóczy International conference on global research and education, 215-227, 2019 | 220 | 2019 |
| Predicting and mapping of soil organic carbon using machine learning algorithms in Northern Iran M Emadi, R Taghizadeh-Mehrjardi, A Cherati, M Danesh, A Mosavi, ... Remote Sensing 12 (14), 2234, 2020 | 213 | 2020 |
| Comprehensive review of deep reinforcement learning methods and applications in economics A Mosavi, Y Faghan, P Ghamisi, P Duan, SF Ardabili, E Salwana, ... | 211 | 2020 |
| Meta-heuristic algorithm-tuned neural network for breast cancer diagnosis using ultrasound images S Bourouis, SS Band, A Mosavi, S Agrawal, M Hamdi | 208 | 2022 |
| Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system M Dehghani, H Riahi-Madvar, F Hooshyaripor, A Mosavi, ... | 207 | 2019 |
| Ensemble boosting and bagging based machine learning models for groundwater potential prediction A Mosavi, F Sajedi Hosseini, B Choubin, M Goodarzi, AA Dineva, ... Water Resources Management 35, 23-37, 2021 | 199 | 2021 |
| Groundwater quality assessment for sustainable drinking and irrigation E Asadi, M Isazadeh, S Samadianfard, MF Ramli, A Mosavi, N Nabipour, ... | 199 | 2019 |
