Prediction of Groundwater Level in Safwan-Zubair Area Using Artificial Neural Networks

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  Ali H. Al-Aboodi   |      Kifah M. Khudhair   |      Ali S. Al-Aidani   |   

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 levels in this area using the ANNs model. The data required for building the ANNs model are generated using the MODFLOW model (V.5.3). MODFLOW model was calibrated based on field measurements of groundwater level in13 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-Marquardt 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 the 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, besides the hydraulic heads has improved slightly the results.