NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

作者：Abdullah Al Jami,Meher Uddin Himel,Khairul Hasan,Shilpy Rani Basak,Ayesha Ferdous Mita

摘要：Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs(NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt(LM) and Bayesian Regularization(BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error(MSE), coefficient of determination(R~2), and Nash-Sutcliffe coefficient of efficiency(NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.

發文機構：Department of Civil and Environmental Engineering

關鍵詞：NARXneuralnetworksArtificialneuralnetworksGroundwaterlevelLevenberg-MarquardtAlgorithm(LMA)BayesianRegularizationAlgorithm(BRA)

分類號： P641.7[天文地球—地質礦產勘探]