Groundwater modeling using hybrid of artificial neural network with genetic algorithm

Accurate estimates of groundwater level have a valuable effect in improving decision support systems of groundwater resources exploitation. The present study investigates the ability of a hybrid model of artificial neural network (ANN) and genetic algorithm (GA) in forecasting groundwater level in an individual well (target well). A standard feed forward networks (FFN) and recurrent neural networks (RNN) are utilized for performing the prediction task. Moreover, GA is used in order to determine the optimal structure of ANN (that is, number of neurons for each hidden layer). Air temperature, rainfall depth and groundwater levels in neighboring wells in Kerman plain (Kerman, Iran) were used as input data of the hybrid model. This study indicates that the ANN-GA model can be used successfully to forecast groundwater levels of individual wells. In addition, a comparative study of both hybrid models indicates that the feed forward networks performed better than the recurrent neural networks.