FORECASTING AND SIMULATION OF INTEGRATED WATER RESOURCE MANAGEMENT PARAMETERS USING ARTIFICIAL INTELLIGENCE

Decision making and planning require tools and methods to forecast the future. In recent years, several powerful algorithms and tools have been developed and they are used in many fields. Conceptual models in water resources management are one of the common methods and recently, intelligence algorithms which are inspired by nature such as artificial neural networks, fuzzy logic, genetic algorithms and so on are developed. Combining these methods causes development of more powerful and more accurate tools. The purpose of this research is development of an integrated water resources management model to optimally supply water to different users, simulate and predict different phenomena such as rainfall-runoff, Alavian reservoir dam operation, groundwater extraction scenarios, and conjunctive use of surface/ground water in Maragheh Plain. At first, all necessary data including rainfall and runoff measurements in stations located in watershed upstream of Alavian Dam collected. Based on the results of these models and considering the travel time to Alavian Reservoir, rule curve for reservoir operation developed. The operation rules considered to normal, wet and dry years. Groundwater of Maragheh Plain based on observation wells, operational wells, geological and hydrogeological data simulated and calibrated using artificial neural network models. The final output would be an integrated water resources model including optimization-simulation models developed in MATLAB software. In this research, using intelligent systems for simulating all phenomena in watershed,genetic algorithm used to optimize the conjunctive use of surface/ground water in Maragheh Plain.

Keywords:

Forecasting , Simulation , Watershed Management Parameters , Neuro-Fuzzy Systems