Assi. Pro. Dr. Hamid Kardan Moghaddam | Medium Access Control | Editorial Member
Assi. Pro. Dr. Hamid Kardan Moghaddam | Water research institute | Iran
Assi. Pro. Dr. Hamid Kardan Moghaddam is an Assistant Professor and leading researcher in water resources, with contributions spanning hydrogeology, groundwater modeling, water quality assessment, and sustainable water management. His work advances aquifer vulnerability evaluation, groundwater level forecasting, coastal aquifer protection, and machine-learning–based prediction of groundwater behavior under climate and consumption pressures. He has developed innovative comparative models using Bayesian networks, artificial neural networks, support vector regression, and hybrid evolutionary algorithms to improve prediction accuracy for groundwater levels, dam inflow, and groundwater storage loss. His research also includes scenario-based decision frameworks, multi-criteria optimization for seawater intrusion mitigation, and regional groundwater sustainability assessment through clustering and numerical simulation. With 1129 citations, h-index 17, and i10-index 29, his studies are widely recognized for supporting sustainable groundwater use and addressing critical water challenges in vulnerable regions.
Profile: Google Scholar
Featured Publications:
Kardan Moghaddam, H., Jafari, F., & Javadi, S. (2017). Vulnerability evaluation of a coastal aquifer via GALDIT model and comparison with DRASTIC index using quality parameters. Hydrological Sciences Journal, 62(1), 137-146.
Moghaddam, H. K., Moghaddam, H. K., Kivi, Z. R., Bahreinimotlagh, M., et al. (2019). Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundwater for Sustainable Development, 9, 100237.
Mozaffari, S., Javadi, S., Moghaddam, H. K., & Randhir, T. O. (2022). Forecasting groundwater levels using a hybrid of support vector regression and particle swarm optimization. Water Resources Management, 36(6), 1955-1972.
Noorbeh, P., Roozbahani, A., & Kardan Moghaddam, H. (2020). Annual and monthly dam inflow prediction using Bayesian networks. Water Resources Management, 34(9), 2933-2951.
Moghaddam, H. K., Milan, S. G., Kayhomayoon, Z., & Azar, N. A. (2021). The prediction of aquifer groundwater level based on spatial clustering approach using machine learning. Environmental Monitoring and Assessment, 193(4), 1-20.