The Kherran plain is located in the northeast of Ahwaz in Khuzestan Province, Iran. The state of groundwater pollution is a critical issue with increasing population and agricultural development in Iran. For this reason, vulnerability assessment is an important factor in any policy making decision in any part of country. Focusing on this issue, the article attempts to presents a groundwater vulnerability map for the Kherran plain. The map designed to show areas of highest potential for groundwater pollution based on hydro-geological condition and human impacts. Seven major hydro-geological factors (Depth to water table, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone and hydraulic Conductivity) were incorporated into DRASTIC model and geographical information system (GIS) was used to create a groundwater vulnerability map by overlaying the available hydro-geological data. The output map shows that the west and southwest of the aquifer are under medium vulnerability while small areas on northwest and east of the study area have no risk to pollution. Other parts of aquifer have low vulnerability. For testing of the vulnerability assessment, 27 groundwater samples were collected from the different vulnerability zones of the study area. The chemical analysis results show that the southwest and west parts of aquifer (moderate vulnerability zones) have higher nitrate concentration relative to the rest of aquifer, that are located in low vulnerability zone.

In the study area, groundwater is the main water resource for various purposes such as drinking, agriculture and industrial. To evaluate the hydrochemical characteristics of groundwater and suitability for drinking, irrigation and industrial purposes, seventy-seven samples were collected and analyzed for various ions. Results show that, groundwater in the study area is mainly hard to very hard, and slightly alkaline-fresh to brackish in nature. According to the hydrochemistry diagrams, the main groundwater types are Ca, Mg-HCO 3 , Na-HCO 3 and Na-Cl. Calculation of mineral saturation index indicate that the groundwater samples are saturated with respect to carbonate minerals and under-saturated with respect to sulfate minerals such as gypsum and anhydride. The mineral weathering, mixing, ion exchange and anthropogenic activity are the dominant hydrogeochemical natural processes. Results of investigating the quality of heavy metals and calculating the heavy metal index indicated that the groundwater of study area is not contaminated with heavy metals. In this research, the various indices were used to determine the quality of groundwater for various uses. Calculate the indices and comparison results with the WHO standards to determine the quality of groundwater for various uses indicated that the most of the groundwater in study area is chemically suitable for drinking, industrial and agricultural uses.

No abstract

The main purpose of this article is to apply feed forward back propagation neural network (FNN) to predict groundwater level of Aghili plain, which is located in southwestern Iran. An optimal design is completed for the two hidden layers with four different algorithms: descent with momentum (GDM), Levenberg Marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG). The training data for ANN is obtained from observation data. Rain, evaporation, relative humidity, temperature, discharge of irrigation canal, and groundwater recharge from the plain boundary were used in input layer while future groundwater level was used as output layer. Before training, the available data were divided into three groups, according to hydrogeological characteristics of different parts of the plain surrounding each piezometer. Statistical analysis in terms of Mean-Square-Error (MSE) and correlation coefficient (R) was used to investigate the prediction performance of ANN. FFN-LM algorithm has shown best result in the present study for all three hydrogeological groups. Now, to predict water level, the t time data () were used as input and output respectively. The best condition of this network was achieved for each group of data. Next, with defining the new input data related to August 2010 to January 2011 groundwater level was predicted for the following year. The achieved results of ANN model in contrast with results of finite difference model showed very high accuracy of artificial neural network in predicting groundwater level.

No abstract