Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels

Abstract

An accurate prediction of groundwater level is a key element in appropriate planning and managing benefits driven from aquifers in watershed. In this study, the conceptual model (MODFLOW), the Bayesian network (BN) and the artificial neural network (ANN) models are employed to forecast monthly groundwater level in Birjand Aquifer, South Khorasan, Iran. In this regard, total monthly evaporation, average temperature, aquifer recharge and discharge, and water table of previous months from 13 observation piezometers and in a 12-year period are used as input variables in order to forecast the groundwater level in the forthcoming months. The results of different models revealed that the Bayesian network models are superior to the ANN and mathematical models. The Bayesian network, ANN and mathematical models developed for the 13 piezometers have an average coefficient of determination of 0.9, 0.76 and 0.72, respectively. The results of this study demonstrate that the BNs are efficient tools for forecasting groundwater level.