Ali Forghani brings experience in the areas of groundwater flow and contaminant transport modeling, heuristic optimization models, machine learning and data analysis, and hydrologic modeling. He specializes in simulation and optimization models to evaluate performance of aquifer and storage recovery (ASR) wells, and the conjunctive use of surface and ground water resources in arid regions. His experience includes employing groundwater flow model and transport simulations to quantify recovery effectiveness (REN) in ASR systems in freshwater aquifers (REN is the proportion of the injected water that is recoverable from the same ASR well); using multivariate adaptive regression splines (MARS) to evaluate the overall REN in an ASR system in Utah; and developing an artificial neural network (ANN) based software as a generalized REN predictor for ASR systems. Ali is proficient in using groundwater-related codes (e.g., MODFLOW and MT3DMS), optimization codes (e.g. GAMS and NSGA2), programming languages (e.g., R, Python, C++, and Fortran), and GUI development tools. He also has experience teaching HEC-HMS and EPANET at several universities.
Ali Forghani, Ph.D.
PhD, Civil and Water Resources Engineering, Utah State University, 2017
MS, Civil & Water Resources Engineering, Sharif University of Technology, 2007
BS, Civil Engineering, Isfahan University of Technology, 2005