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

Authors: Ali H. Al-Aboodi علي حسن دهيم --- Kifah M. Khudhair --- Ali S. Al-Aidani
Journal: Basrah Journal for Engineering Science مجلة البصرة للعلوم الهندسية ISSN: Print: 18146120; Online: 23118385 Year: 2016 Volume: 16 Issue: 1 Pages: 42-50
Publisher: Basrah University جامعة البصرة

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Abstract

Safwan-Zubair area is regarded as one of theimportant agricultural areas in Basrah province, South of Iraq.The aim of this study is to predict groundwater level in this areausing ANNs model. The data required for building the ANNmodel are generated using MODFLOW model (V.5.3).MODFLOW model was calibrated based on field measurementsof groundwater level in13 monitoring wells during a period ofone year (Nov./2013 to Oct/2014). The neural network toolboxavailable in MATLAB version 7.1 (2010B) was used to developthe ANN models. Three layers feed-forward network with Log-sigmoid transfer function was used. The networks were trainedusing Levenberg-Marquardt back-propagation algorithm. TheANN 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 andrecharge rates. Based on results of this study it was found that;the best ANN model for predicting groundwater levels in thestudy area is obtained when the input data includes hydraulicheads and recharge rates of two successive months preceding thetarget month, the best structure of ANN model is of three layersfeed-forward network type composes of two hidden layers, eachof ten nodes, and the including of recharge rates as input data,beside the hydraulic heads has improved slightly the results.

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