Enhanced Forecasting of Groundwater Level Incorporating an Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models

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Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels (GWLs). As a part of sustainable planning and effective management of water resources, it is crucial to assess the existing and forecasted GWL conditions. In this study, an attempt was made to model and forecast GWL using artificial neural networks (ANNs) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA models incorporating exogenous variables (ARIMAX) were adopted as the time series models. GWL data from five monitoring wells from the study area of the Kushtia District in Bangladesh were used to demonstrate the modeling exercise. Rainfall (RF) was taken as the exogenous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. It was evident from the results that the multivariate ARIMAX model (with the sum of squared errors, SSE, of 15.143) performed better than the univariate ARIMA model with an SSE of 16.585 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the models, it was found that the ANN-based model outperformed the time series models with enhanced forecasting accuracy (SSE of 9.894). The results also exhibit a significant correlation coefficient (R) of 0.995 (model ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area.