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Go to Editorial ManagerGroundwater in arid and semi-arid regions, such as the studied area (Safwan Al-Zubair area, south of Iraq), is of specific meaning as a major source for domestic use and irrigation demand. There is a need to better understand the interactions between groundwater and surface water (Shatt Al-Basrah Canal). These interactions can negatively affect the quality of groundwater in this area, especially that the water of Shatt Al- Basrah Canal contains highly concentrated pollutants. The aim of the study is to investigate the temporal disparity of river-aquifer interactions and count the amount of river interchange among canal and aquifer. In this research, a new concept of paradigm will be advanced utilizing RIVER package of Groundwater River Paradigm (MODFLOW) for the simulation of river-aquifer interaction operations. Six monitoring wells are chosen to evaluate the preliminary and historical groundwater hydraulic heads for six months and then use all collected data in Modflow to execute the simulation of numerical modeling to assessment the interaction between surface water and groundwater. The amount of seepage out from the canal towards the aquifer was (64.99 m 3 /day) in wet season (winter season), as a result of the high levels of the surface water compared to the hydraulic heads of groundwater. The amount of seepage in dry season towards the aquifer is equal to (336.8 m 3 /day).
Safwan-Zubair area is regarded as one of the important agricultural areas in Basrah province, South of Iraq. The aim of this study is to predict groundwater level in this area using ANNs model. The data required for building the ANNs model are generated using MODFLOW model (V.5.3). MODFLOW model was calibrated based on field measurements of groundwater level in 13 monitoring wells during a period of one year (Nov./2013 to Oct/2014). The neural network toolbox available in MATLAB version 7.1 (2010B) was used to develop the ANN models. Three layers feed-forward network with Log- sigmoid transfer function was used. The networks were trained using Levenberg-Marquradt back-propagation algorithm. The ANN modes are divided into two groups, each of four models. The input data of the first group include hydraulic heads, while, the input data of the second group include hydraulic heads and recharge rates. Based on results of this study it was found that; the best ANN model for predicting groundwater levels in the study area is obtained when the input data includes hydraulic heads and recharge rates of two successive months preceding the target month, the best structure of ANN model is of three layers feed-forward network type composes of two hidden layers, each of ten nodes, and the including of recharge rates as input data, beside the hydraulic heads has improved slightly the results.
A two-dimensional mathematical model has been constructed by using finite difference method for representation the groundwater flow in both steady and unsteady states at the upper aquifer of Dibdibba formation. The hydraulic characteristics of this aquifer have been redistributed based on observed data for the period (1988• l 989). A verification test is added to check the model correctness by matching the calculoted levels with the ones observed for the year 2000.A model was set to predict the groundwater levels up to the year 2010. Results of prediction show a reduction in groundwater level about (Im) in the central parts of the study area compared to the level of this groundwater in the year 2000.0n the other hand, this decrease is reaches (0.5m) in the western parts of this area.