The chemical quality of groundwater of Bushehr, southwest of Iran, was assessed for its suitability for drinking purposes. Hydro-geochemical studies were carried out in this area to identify the geochemical processes and their relation to groundwater quality. A total of 19 water samples were collected from the aquifer. The samples were then analyzed for different physicochemical properties, such as pH, total dissolved solids (TDS), total hardness (TH), calcium, magnesium, carbonate, bicarbonate, sulfate, and chloride concentrations. In this study, the average TDS content was in the range of 4419-10,066 mg l/l, and other important parameters of water, such as TH (1200-3500 mg l/1) and chloride (1046-3855 mg l/1), were also higher than the maximum permissible limits specified by WHO. On the basis of concentrations of major elements, studies of the study area showed that the total samples collected are unsuitable for drinking. Linear increase in sodium and chloride of the total dissolved ion indicated a dissolution of halite in the study area. Salinity of the aquifer is mainly a result of the Dalaki River recharge, dissolution of evaporated minerals intraformation, and also agricultural returned water. The dissolution of evaporite minerals, such as halite and gypsum, has increased the concentration of total dissolved solids and of sulfate in the Shahpour River and also groundwater entering the study area has caused salinity in this river.

No abstract

Accurate and reliable groundwater level prediction is an important issue in groundwater resource management. The objective of this research is to compare groundwater level prediction of several data-driven models for different prediction periods. Five different data-driven methods are compared to evaluate their performances to predict groundwater levels with 1-, 2- and 3-month lead times. The four quantitative standard statistical performance evaluation measures showed that while all models could provide acceptable predictions of groundwater level, the least square support vector machine (LSSVM) model was the most accurate. We developed a set of input combinations based on different levels of groundwater, total precipitation, average temperature and total evapotranspiration at monthly intervals. For each model, the antecedent inputs that included Ht-1, Ht-2, Ht-3, Tt, ETt, Pt, Pt-1 produced the best-fit model for 1-month lead time. The coefficient of determination (R2) and the root mean square error (RMSE) were calculated as 0.99%, 1.05 meters for the train data set, and 95%, 2.3 meters for the test data set, respectively. It was also demonstrated that many combinations the above-mentioned approaches could model groundwater levels for 1 and 2 months ahead appropriately, but for 3 months ahead the performance of the models was not satisfactory.

No abstract

No abstract