Application and Analysis of physical and data-driven stochastic hydrological Simulation-Optimization Methods for the optimal Management of Surface-Groundwater Resources Systems: Iranian Cases Studies

Application of physical and data-driven stochastic hydrological simulation-optimization techniques is a major title in water resources planning for the prediction of future water-affecting events and conditions. Appropriate models and algorithms such as conceptual surface-groundwater models, artificial intelligence models (ANN), swarm intelligence and fuzzy logic have been developed and used in many research projects of conjunctive management of surface-groundwater resources in recent years. The use of these models and algorithms leads to an increased accuracy in the modeling of water resources allocation problems, as will be shown in the present thesis by applying various physical and data-driven stochastic hydrological simulation-optimization methods to the optimal management of surface-groundwater resources systems to several Iranian regions which are increasingly being subjected to water stresses in recent decades, not to the least due to ongoing climate change in this part of the world. The combination of these methodologies/case studies has led to 8 individual research publications, either published or in press at the time of this writing.