| Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model R Barzegar, MT Aalami, J Adamowski Stochastic Environmental Research and Risk Assessment 34, 415–433, 2020 | 387 | 2020 |
| Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models R Barzegar, E Fijani, A Asghari Moghaddam, E Tziritis Science of the Total Environment 599 (600C), 20-31, 2017 | 215 | 2017 |
| Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran R Barzegar, J Adamowski, AA Moghaddam Stochastic Environmental Research and Risk Assessment 30 (7), 1797-1819, 2016 | 180 | 2016 |
| Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms R Barzegar, A Asghari Moghaddam, R Deo, E Fijani, E Tziritis Science of the Total Environment 621 (C), 697–712, 2018 | 172 | 2018 |
| Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of … E Fijani, R Barzegar, R Deo, E Tziritis, K Skordas Science of the Total Environment 648, 839-853, 2019 | 165 | 2019 |
| Coupling a Hybrid CNN-LSTM Deep Learning Model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for Multiscale Lake Water Level Forecasting R Barzegar, MT Aalami, J Adamowski Journal of Hydrology, 126196, 2021 | 135 | 2021 |
| Identification of hydrogeochemical processes and pollution sources of groundwater resources in the Marand plain, northwest of Iran R Barzegar, A Asghari Moghaddam, E Tziritis, MS Fakhri, S Soltani Environmental Earth Sciences, 1-17, 2017 | 120 | 2017 |
| Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model R Barzegar, AA Moghaddam, J Adamowski, B Ozga-Zielinski Stochastic Environmental Research and Risk Assessment, 1-15, 2018 | 115 | 2018 |
| Assessing the potential origins and human health risks of trace elements in groundwater: A case study in the Khoy plain, Iran R Barzegar, A Asghari Moghaddam, J Adamowski, AH Nazemi Environmental Geochemistry and Health 41 (2), 981–1002, 2019 | 114 | 2019 |
| A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran R Barzegar, AA Moghaddam, H Baghban Stochastic Environmental Research and Risk Assessment 30 (3), 883-899, 2016 | 99 | 2016 |
| Heavy Metal(loid)s in the Groundwater of Shabestar Area (NW Iran): Source Identification and Health Risk Assessment R Barzegar, A Asghari Moghaddam, S Soltani, E Fijani, E Tziritis, ... Exposure and Health 11, 251–265, 2019 | 98 | 2019 |
| Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction R Barzegar, A Asghari Moghaddam Modeling Earth Systems and Environment 2, 1-13, 2016 | 98 | 2016 |
| Assessing the hydrogeochemistry and water quality of the Aji-Chay River, northwest of Iran R Barzegar, AA Moghaddam, E Tziritis Environmental Earth Sciences 75 (23), 1-15, 2016 | 93 | 2016 |
| Comparison of machine learning models for predicting fluoride contamination in groundwater R Barzegar, AA Moghaddam, J Adamowski, E Fijani Stochastic Environmental Research and Risk Assessment 31 (10), 2705–2718, 2017 | 89 | 2017 |
| Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones DK Roy, R Barzegar, J Quilty, J Adamowski Journal of Hydrology 591, 125509, 2020 | 67 | 2020 |
| Risk assessment and ranking of heavy metals concentration in Iran’s Rayen groundwater basin using linear assignment method A Rezaei, H Hassani, M Hayati, N Jabbari, R Barzegar Stochastic Environmental Research and Risk Assessment, 2018 | 65 | 2018 |
| Assessment of heavy metals concentrations with emphasis on arsenic in the Tabriz plain aquifers, Iran R Barzegar, A Asghari Moghaddam, N Kazemian Environmental Earth Sciences 74, 297-313, 2015 | 65 | 2015 |
| Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, case study: Azarshahr area, NW Iran R Barzegar, M Sattarpour, MR Nikudel, AA Moghaddam Modeling Earth Systems and Environment 2, 1-13, 2016 | 60 | 2016 |
| An ensemble tree-based machine learning model for predicting the uniaxial compressive strength of travertine rocks R Barzegar, M Sattarpour, R Deo, E Fijani, J Adamowski Neural Computing and Applications 32, 9065–9080, 2020 | 58 | 2020 |
| Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models R Barzegar, S Razzagh, J Quilty, J Adamowski, H Kheyrollah Pour, ... Journal of Hydrology, 2021 | 57 | 2021 |
Asghar Asghari MoghaddamProfessor of Hydrogeology, University of TabrizVerified email at tabrizu.ac.ir
