Spatiotemporal Groundwater Level Forecasting in Coastal Aquifers by Hybrid Artificial Neural Network-Geostatistics Model: A Case Study

Abstract

Abstract

Prediction of groundwater level in a basin is of immense importance for the management of groundwater resources, especially in coastal regions where the water table fluctuations are to be limited to avoid sea water intrusion. Lack of strong predictive tools, or perhaps the lack of experienced users of those tools, may contribute to problems in data interpretation and failure to reach consensus about the need for key water management actions. Therefore, it is extremely important to comprehend the spatiotemporal variations of the water level for the management of groundwater in the coastal areas. In this article, a hybrid, artificial neural network-geostatistics methodology is presented for spatiotemporal prediction of groundwater levels. The proposed model contains two separated stages. At the first stage, an artificial neural network is trained for each piezometer for time-series modeling of the water level, so that the model can predict the groundwater level the next month. At the second stage, predicted values of water levels at different piezometers are imposed to a calibrated geostatistics model in order to estimate groundwater level at any desired point in the plain. This methodology is applied for the Shabestar plain, which adjoins to Urmieh Lake as a coastal aquifer in East Azerbaijan Province, Iran. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and available data series. Results suggest that the feed-forward neural network trained with Levenberg-Marquardt algorithm for temporal and Kriging scheme for spatial modeling are good choices for predicting groundwater levels in the coastal aquifer.