Google scholar : https://scholar.google.com/citations?user=eBkii4gAAAAJ&hl=en
ResearchGate : https://www.researchgate.net/profile/Rahim-Barzegar-2
FORMATION
- Ph. D. en hydrogéologie (Université de Tabriz, 2019)
- M. Sc. en hydrogéologie (Université de Tabriz, 2014)
- B. Sc. géologie (Université de Tabriz, 2012)
Rahim Barzegar est professeur adjoint à l'Institut de recherche sur les mines et l'environnement (IRME) de l'Université du Québec en Abitibi-Témiscamingue (UQAT). Il est titulaire d'un doctorat en hydrogéologie de l'Université de Tabriz en Iran. Il a également complété des bourses postdoctorales à l'Université McGill, à l'Université Wilfrid Laurier et à l'Université de Waterloo au Canada. Son principal domaine de recherche se concentre sur l'exploration de nouvelles approches en modélisation hydro(géo)logique et environnementale, en particulier en utilisant des techniques d'apprentissage automatique et d'apprentissage en profondeur. De plus, il participe activement à d'autres projets de recherche tels que l'analyse et la modélisation de séries chronologiques, l'évaluation de la qualité de l'eau, l'évaluation de la vulnérabilité et des risques des eaux souterraines, la gestion des ressources en eau et l'examen des impacts du changement climatique sur les ressources en eau.
PUBLICATIONS RÉCENTES
Gharekhani, M., Nadiri, A. A., Khatibi, R., Nikoo, M. R., Barzegar, R., Sadeghfam, S., & Moghaddam, A. A. (2023). Quantifying the groundwater total contamination risk using an inclusive multi-level modelling strategy. Journal of Environmental Management, 332, 117287.
Nadiri, A. A., Aghdam, F. S., Razzagh, S., Barzegar, R., Jabraili-Andaryan, N., & Senapathi, V. (2022). Using a soft computing OSPRC risk framework to analyze multiple contaminants from multiple sources; a case study from Khoy Plain, NW Iran. Chemosphere, 308, 136527.
Khosravi, K., Phuong, T. T., Barzegar, R., Quilty, J., & Aalami, M. T. (2022). Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions. Pedosphere, 32(5), 718-732.
Nadiri, A. A., Sedghi, Z., Barzegar, R., & Nikoo, M. R. (2022). Establishing a Data Fusion Water Resources Risk Map Based on Aggregating Drinking Water Quality and Human Health Risk Indices. Water, 14(21), 3390.
Nadiri, A. A., Barzegar, R., Sadeghfam, S., & Rostami, A. A. (2022). Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI. Water, 14(19), 3185.
Yang, L., Feng, Q., Wen, X., Barzegar, R., Adamowski, J. F., Zhu, M., & Yin, Z. (2022). Contributions of climate, elevated atmospheric CO2 concentration and land surface changes to variation in water use efficiency in Northwest China. Catena, 213, 106220.
Panahi, M., Khosravi, K., Golkarian, A., Roostaei, M., Barzegar, R., Omidvar, E., Rezaie, F., Saco, P.M., Sharifi, A., Jun, C. and Bateni, S.M. (2022). A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning. Geocarto International, 1-23.
Nadiri, A. A., Habibi, I., Gharekhani, M., Sadeghfam, S., Barzegar, R., & Karimzadeh, S. (2022). Introducing dynamic land subsidence index based on the ALPRIFT framework using artificial intelligence techniques. Earth Science Informatics, 15(2), 1007-1021.
Barzegar, R., Razzagh, S., Quilty, J., Adamowski, J., Pour, H. K., & Booij, M. J. (2021). Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models. Journal of hydrology, 598, 126370.
Liu, W., Yang, L., Zhu, M., Adamowski, J. F., Barzegar, R., Wen, X., & Yin, Z. (2021). Effect of elevation on variation in reference evapotranspiration under climate change in Northwest China. Sustainability, 13(18), 10151.
Khosravi, K., Barzegar, R., Golkarian, A., Busico, G., Cuoco, E., Mastrocicco, M., Colombani, N., Tedesco, D., Ntona, M.M. and Kazakis, N. (2021). Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer. Journal of Contaminant Hydrology, 242, 103849.
Barzegar, R., Aalami, M.T., Adamowski J. (2021). Coupling a Hybrid CNN-LSTM Deep Learning Model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for Multiscale Lake Water Level Forecasting. Journal of Hydrology, 598, 126196.
Khosravi, K., Golkarian, A., Booij, M. J., Barzegar, R., Sun, W., Yaseen, Z. M., & Mosavi, A. (2021). Improving daily stochastic streamflow prediction: Comparison of novel hybrid data-mining algorithms. Hydrological sciences journal, 66(9), 1457-1474.
Roy, D. K., Barzegar, R., Quilty, J., & Adamowski, J. (2020). Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones. Journal of Hydrology, 591, 125509.
Jahromi, M.N., Gomeh, Z., Busico, G., Barzegar, R., Samany, N.N., Aalami, M.T., Tedesco, D., Mastrocicco, M. and Kazakis, N. (2021). Developing a SINTACS-based method to map groundwater multi-pollutant vulnerability using evolutionary algorithms. Environmental Science and Pollution Research, 28, 7854-7869.
Barzegar, R., Aalami, M. T., & Adamowski, J. (2020). Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model. Stochastic Environmental Research and Risk Assessment, 34(2), 415-433.
Barzegar, R., Moghaddam, A.A., Tziritis, E., Adamowski, J., Nassar, J.B., Noori, M., Aalami, M.T. and Kazemian, N. (2020). Exploring the hydrogeochemical evolution of cold and thermal waters in the Sarein-Nir area, Iran using stable isotopes (δ18O and δD), geothermometry and multivariate statistical approaches. Geothermics, 85, 101815.
Khosravi, K., Barzegar, R., Miraki, S., Adamowski, J., Daggupati, P., Alizadeh, M.R., Pham, B.T. and Alami, M.T. (2020). Stochastic modeling of groundwater fluoride contamination: Introducing lazy learners. Groundwater, 58(5), 723-734.
Barzegar, R., Sattarpour, M., Deo, R., Fijani, E., & Adamowski, J. (2020). An ensemble tree-based machine learning model for predicting the uniaxial compressive strength of travertine rocks. Neural Computing and Applications, 32, 9065-9080.
Barzegar, R., Asghari Moghaddam, A., Norallahi, S., Inam, A., Adamowski, J., Alizadeh, M. R., & Bou Nassar, J. (2020). Modification of the DRASTIC framework for mapping groundwater vulnerability zones. Groundwater, 58(3), 441-452.
Barzegar, R., Ghasri, M., Qi, Z., Quilty, J., & Adamowski, J. (2019). Using bootstrap ELM and LSSVM models to estimate river ice thickness in the Mackenzie River Basin in the Northwest Territories, Canada. Journal of Hydrology, 577, 123903.
Barzegar, R., Asghari Moghaddam, A., Adamowski, J., & Nazemi, A. H. (2019). Assessing the potential origins and human health risks of trace elements in groundwater: a case study in the Khoy plain, Iran. Environmental Geochemistry and Health, 41, 981-1002.
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