a conceptual framework of a surrogate-based quality-quantity decision support system (q2dss) for water resources systems

The water crisis in different countries of the world has made the earth undergo tremendous changes compared to the past years. therefore, it is very necessary to have intelligent systems that can help managers make correct and optimal decisions in various possible conditions. in recent years, the biggest challenge faced by water resource managers in the karun basin in iran has been the decline in the quality of surface waters in the downstream areas of the basin. in this research, a surrogate-based model has been developed for predicting and controlling the quantity and quality of water in different parts of the basin. as a decision support system, this model can evaluate the quantity of water at different points in the basin and also predict its quality in various probable conditions. this model will also be used to extract optimal operating policies with the aim of satisfying quality constraints in different conditions. the model can help decision makers in the optimal management of the system and also greatly reduce the losses caused by quality issues in possible future situations.