Hybrid Deep Learning Models for Predicting Saltwater Intrusion in Nearshore Aquifers: Comparative Evaluation of CNN, LSTM, and DNN Architectures

Saltwater intrusion (SI) threatens groundwater sustainability in nearshore regions, particularly in Bangladesh, where over-extraction and sea-level rise accelerate aquifer salinization. Accurate prediction of SI dynamics is therefore critical for effective groundwater management. This study developed and evaluated several deep learning and hybrid models, including CNN, DNN, LSTM, CNN–DNN, CNN–LSTM, DNN–LSTM, and CNN–DNN–LSTM, to predict SI in a nearshore aquifer system. Predictor–response datasets were generated using the three-dimensional density-dependent flow and solute transport model FEMWATER. This study presents the first comprehensive benchmarking of standalone and hybrid CNN–DNN–LSTM models for SI prediction in a Bangladesh nearshore aquifer, supported by CRITIC–EDAS-based model ranking. Model performance was assessed using RMSE, MAE, MAD, R, IOA, a-20, NRMSE, along with CRITIC weighting and EDAS ranking. Results indicate that hybrid models integrating LSTM outperformed standalone models. The CNN–LSTM model achieved the best performance at OW1 (RMSE = 1.57 mg/L, MAE = 1.26 mg/L, R = 0.99, IOA = 0.99). The DNN–LSTM model performed best at OW2 (RMSE = 2.87 mg/L, IOA = 0.98, R = 0.97) and OW3 (RMSE = 1.95 mg/L, IOA = 0.99, R = 0.99). In contrast, the DNN model showed poor performance, while the CNN model demonstrated moderate performance and the LSTM model underperformed. Overall, the hybrid CNN–LSTM and DNN–LSTM models demonstrated superior accuracy and robustness for reliable SI prediction and sustainable groundwater management.