Forecasting groundwater level of Shahrood plain in Iran with stochastic and artificial neural network models

In the water resources systems planning, management and prediction of groundwater level is an important parameter. Several techniques such as stochastic models, fuzzy models, artificial neural networks and others methods can be used for this purpose. Stochastic model is one these techniques that formed based on time series. And also an Artificial Neural Networks (ANNs) is flexible computing frameworks and universal approximates that can be applied to a wide range of forecasting problems with a high degree of accuracy. Therefore in this study ANNs and stochastic models used for predicting groundwater level (GWL) fluctuations of Shahrood Plain in Iran. For this purpose the rain, relative humidity, temperature, evaporation, temperature, the rivers inflow of Mojen and Tash, the river outflow of Ghaleno and groundwater level data as monthly collected at the study area and these data were used to train and validate the ANN model. The ANN model was performed by varying the network parameters to minimize the prediction error and determine the optimum network configuration. Also in this research different stochastic models are fitted to monthly data of groundwater level. After performance of necessary tests, PARMA (2, 1) model with the least Akaike Information Criterion (AIC) and the Schwarz Information Criterion (SIC) has been selected as suitable model. The results show that the performance of the MLP/BP neural network was good in predicting the groundwater level rather than stochastic model. Therefore it can be used for proper water management studies in that area.