مهندس احسان یارمحمدی

Groundwater quality can be evaluate by comparing measured quality parameters to prepared standards. This method although looks simple, but it doesn't show a comprehensive view of water quality condition in arid and semiarid areas. In current research after calculations of groundwater quality of Mehran plain using IRWQI, the ability of different geostatistical methods have been investigated for quality zoning.

Based on the importance of groundwater resources in Mehran plain, 17 piezometer wells were selected. Afterward, sampling and analysis have been done during February 2020. For calculating IRWQI index, all measured quality parameters were weighted based on the roles in groundwater pollution. Pearson correlation coefficient was used to evaluate the correlation between data and IRWQI index. Then, for zoning groundwater quality different geostatistic methods including Inverse Distance Weighting, Global polynomial interpolation, Radial Basis Function, Local Polynomial Interpolation, Ordinary Kriging, Simple Kriging and Universal Kriging have been used.

The results show a significant correlation between water quality index and EC and SAR parameters at the level of 1 and 5%. Results show that, based on IRWQI, the quality of groundwater in the plain is very good by mean index of 100. In another hand, the relationship between groundwater quality parameters and IRWQI showed that EC and SAR have significant correlations. Also, based on the results, IDW has the minimum error by 10.10, 12.65 and 0.77 for MAE, RMSE and R2, respectively, comparing to other geostatistical methods and it has been selected as the best method for Mehran plain groundwater quality zoning.

Groundwater zoning map also indicated that the very good class with the area of 12429 hectare (%69) covered the maximum area of the plain and shows a very good quality of groundwater in whole plain.

In this research, the simulation and optimization models are integrated to apply the reservoir hedging policy. Simulation of the studied basin was executed using the WEAP model to operate the Doiraj Dam reservoir located on the Doiraj River. In addition, the multi-objective MOICA model was utilized to optimize the system, in which the first objective function (maximizing the percentage of supplying demands), and the second one (minimizing the violation of allowable capacities of the reservoir during the operation period) were considered. In this regard, the operation modeling from the reservoir was carried out based on the current condition for a 720-month period (October 1960 to September 2019). Finally, by defining the optimization scenario and applying the reservoir hedging policy, the operation optimization of the reservoir was done and the results were compared with the reference scenario results. In this study, by considering 24 decision variables including 12 hedging level variables and 12 hedging coefficient variables, the optimal answers were achieved after 1000 iterations. The results showed that the violation of the allowable capacities did not occurred in any periods of the optimization scenario, while in the reference scenario the reservoir level reached the dead level in sequent months with more water shortage which might lead to the lack of water supply in such months and serious damages to the system. Due to the application of hedging policy in the optimization scenario, the percentage of supplying the demands in the critical months is increased between 20 to 35% as compared to the reference scenario, which indicates a significant reduction in the failure rate in such months.

Optimization of artificial intelligence (AI) models is a significant issue because it enhances the performance and flexibility of the numerical models. In this study, scour depth around bridge abutments with different shapes was estimated by means of ANFIS and ANFIS-Genetic Algorithm. In other words, the membership functions of the ANFIS model were optimized using the genetic algorithm, finding that the performance of ANFIS model was increased. Firstly, effective input parameters on the scour depth around bridge abutments were defined. Then, by using the input parameters, eleven ANFIS and ANFIS-GA models were produced. Next, the superior ANFIS and ANFIS-GA models were introduced by analyzing the numerical results. For example, the correlation coefficient and scatter index for ANFIS model were calculated to be 0.979 and 0.070; for ANFIS-GA, these were 0.986 and 0.056, respectively. In addition, the average discrepancy ratio (DRave) for ANFIS and ANFIS-GA models was 0.984 and 0.988, respectively. Also, it was shown that the ANFIS-GA models had more accuracy, as compared to the ANFIS models. Moreover, a sensitivity analysis showed that Froude number (Fr) and ratio of flow depth to radius of scour hole (h/L) were the most influential input parameters for simulating the scour depth around bridge abutments.