Recovering the Salinity Distributed Sources Into River from Aquifer Using the Simulation-Optimization Method

Due to the increase in population and the need for water supply, preservation and protection of surface water and groundwater resources has been considered by governments. One of the pollutant sources in rivers is entering salinity from groundwater into the river, that in this research is considered as distributed (non-point) sources. The goal is to identify the salinity intensity, location and length of sources by measuring the temporal distribution of concentration in one observation point. For this purpose, the inverse solution of advection-dispersion equation in the river was employed using the simulation-optimization approach. MIKE11 numerical model was used to simulate flow and transfer of salinity in the river, and genetic algorithm was employed for optimization. In the proposed model, considering only one observation point with some measured intensity data for recovering several sources, unknown location and length of the sources, in addition to their intensities is the most significant advantage of the present study. The model verified by using hypothetical examples, 40 km section of the Karun River and also by applying five and 15 percent noise to the observation data. The results confirm the ability of the model to recover the specifications of several distributed sources using only one observation point. With five percent of noise in the observation data, all three specifications of sources can be recovered with the desired accuracy. While at 15 percent of noise, the accuracy of the model in recovering the location and length of sources was decreased. Also, to recover the specifications of each source, employing only three points of the measured data in the ascending part are sufficient.