Groundwater level simulation using artificial neural network: a case study from Aghili plain, urban area of Gotvand, south-west Iran

In this paper, the Artificial Neural Network (ANN) approach is applied for forecasting groundwater level fluctuation in Aghili plain, southwest Iran. An optimal design is completed for the two hidden layers with four different algorithms: gradient descent with momentum (GDM), levenberg marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG). Rain, evaporation, relative humidity, temperature (maximum and minimum), 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. Therefore, FFN-LM algorithm has shown best result in the present study for all three hydrogeological groups. At last, to evaluate applied division, a unit network with all data and using LM algorithm was trained. Validation of the network shows that dividing the piezometers into different groups of data and designing distinct networks gives more focus on simulating groundwater level in the plain. The degree of accuracy of the ANN model in prediction is acceptable. Thus, it can be determined that ANN provides a feasible method in predicting groundwater level in Aghili plain.