The estimation of river runoff is a complex process, but it is of vital importance to the proper operation of reservoirs, the design of hydraulic structures, flood control, drought management and the supply of water and electricity. The high uncertainty in rainfall-runoff modelling and lack of data has made the development of rainfall-runoff models with acceptable levels of accuracy and precision challenging. Furthermore, the rainfall-runoff models commonly do not provide an explicit relationship between runoff and other variables to be used for runoff related investigations. To overcome the knowledge and information shortage in rainfall-runoff modelling, data-driven models have been used instead of conceptual models for the development of rainfall-runoff models. In this paper, three data-driven models, the genetic algorithm-support vector regression (GA-SVR), genetic algorithm-artificial neural network (GA-ANN) and the group method of data handling (GMDH) have been used to predict the monthly runoff of the Gavehroud basin. Their performances are compared with a conceptual hydrological model (HYMOD) whose parameters are calibrated using the GA. To this end, the monthly data on precipitation, temperature and runoff at the Gavehroud basin over 49 yr (1960-2009) were analysed. Evaluation of the results using performance evaluation indicators showed that the hybrid model of GA-SVR provided better accuracy in predicting the nonlinear behaviour of flow data than the GA-ANN, GMDH and HYMOD.

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In this study, 28 groundwater and 13 soil samples from Ahangaran mining district in Hamedan Province, west of Iran were collected to evaluate the level of contamination. Average concentrations of As, Cu, Pb, Zn, Mn, Sb, and Ni in groundwater samples were 1.39, 3.73, 2.18, 9.37, 2.35, 4.44, and 5.50 μg/L (wet season), and 11.64, 4.92, 4.32, 14.77, 5.43, 4.12, and 0.98 μg/L (dry season), respectively. Results of groundwater samples analysis showed that the average of analyzed metals in the wet and dry seasons were below the permissible limits, except As in the dry season which displays concentrations that exceed US EPA water quality criteria recommended for drinking water. Also, the heavy metal pollution index (HPI) values in each sampling station were less than the critical index limit and were suitable for drinking. Factor analysis revealed that variables influential to groundwater quality in one season may not be as important in another season. Average concentrations of Ag, As, Cd, Cu, Pb, Sb, and Zn in soil samples were 2.61, 31.44, 0.51, 55.90, 1284.9, 21.26, and 156.04 mg kg(-1), respectively. The results of the geoaccumulation index (I geo) showed the following decreasing order: Pb > Zn > Cu > As > Sb > Cd > Ag. Potential ecological risk index (RI) suggests that the contamination in the investigated area is moderate to very high risk and the ranking of the contaminants in decreasing order is Ag > Sb > Pb > Cd > As > Cu > Zn.