Roy, Dilip Kumar, Paul, Chitra Rani, Haque, Md Panjarul, and Datta, Bithin (2025) Projection of groundwater level fluctuations using deep learning and dynamic system response models in a drought affected area. Earth Science Informatics, 18 (1). 136.

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Abstract

Groundwater is an essential resource for agriculture and domestic use in drought-prone regions, particularly in northwestern Bangladesh. Accurate forecast of groundwater level (GWL) fluctuations is crucial for sustainable water regulation. This work investigates the application of deep learning and dynamic system response models to forecast GWL changes in this vulnerable area. The models employed include Long Short-Term Memory (LSTM) networks, Autoregressive Moving Average (ARMA), Discrete-Time State-Space Model (n4sid), Continuous-Time State-Space Model (SSEST), Discrete-Time State-Space Model through a Regularized ARX Model Reduction (SSREGEST), and coupled ARMA-state-space models. A total of eight models were trained and tested on historical GWL data from 19 observation wells. The top-performing models at various locations delivered satisfactory results, with C, IOA, NRMSE, and MAD values ranging from 0.53 to 0.92, 0.62 to 0.95, 0.01 to 0.25, and 0.08 m to 1.09 m, respectively. Model comparison using the Entropy-Distance from Average Solution (Entropy-EDAS) method revealed that LSTM networks outperformed traditional time series (ARMA), system dynamic (n4sid, SSEST, SSREGEST), and coupled ARMA-state-space models (ARMA-n4sid, ARMA-SSEST, and ARMA-SSREGEST) in most locations, while other models exhibited varying performances across different observation wells. The varying performance across different observation wells highlights that prediction accuracy depends not only on the modeling algorithms but also on the quantity and quality of the learning and testing data. The projections generated by the best models effectively captured historical trends, providing the first-ever five-year forecasts of GWL fluctuations for the region. These projections offer valuable insights for water resource management and planning in areas vulnerable to drought and climate variability.

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