Optimization design of quality monitoring network of Urmia plain using genetic algorithm and vulnerability map

Contamination and seawater intrusion are esteemed as chief issues of coastal aquifers resulting from unscientific utilization, non-standard monitoring network and improper management. In the present study, an optimum monitoring network with appropriate numbers and standard spatial distribution was designed based on a vulnerability map of the Urmia coastal aquifer. A vulnerability map was extracted using a modified GALDIT-iP model and searching optimum network was done based on genetic algorithm (GA). The maximum value of the correlation between electrical conductivity (EC) and vulnerability index, the minimum number of monitoring wells and the highest value of Nash-Sutcliff were utilized for the simultaneous optimization model. The W-weighting coefficient was considered for economical goals and three targets were defined in a general objective function. The results showed that the W-weighting coefficient has a significant effect to determine optimal solution, and the best weighting was opted considering the most optimal response based on the precise of the monitoring network and vulnerability index. An acceptable optimization and validation process was obtained with the predictions of the validation results. For W = 1, the final value of the objective function was obtained 1.791 using 91 wells with a correlation coefficient of 0.935 and Nash-Sutcliff of 0.979, resulting in the appropriate spatial distribution of the wells and reduction of 18 wells from the existing monitoring wells