Dissolved Oxygen (DO) is a fundamental indicator of the health of aquatic ecosystems. This study introduces a novel spatial-temporal attention model combined with Kernel Mean Squared Error loss function (STA-KMSE) for forecasting short-term DO. An attention-based Convolutional Neural Network (A-CNN) is used to incorporate the influence of upstream stations in the spatial module, a factor often overlooked in prior studies. The temporal module captures the influence of both previous hours and days using attention-based Long Short-Term Memory (A-LSTM), offering a more comprehensive temporal context. Our approach addresses the issue of outlier sensitivity and non-linear error patterns by utilizing the KMSE loss function, which improves the model's robustness and more effectively captures intricate variations in DO levels compared to the standard Mean Squared Error (MSE) commonly used in water quality modelling. The model is trained on an extensive dataset consisting of various water quality parameters collected from twenty locations along the Ganga River, its tributary, and nallahs spanning from 1 April 2017 to 30 April 2021. In terms of RMSE, STA-MSE outperforms A-CNN by 62.669 % and A-LSTM by 7.419 %. Compared to STA-MSE, the STA-KMSE shows an average reduction in RMSE by 3.067 % for one-step, 3.197 % for two-step, and 3.428 % for three-step forecasting. Experiments indicate better performance for river locations than for nallahs. SHapley Additive exPlanation (SHAP) analysis highlights pH and temperature as crucial factors for estimating DO in rivers, while level, temperature, conductivity, total suspended solids, pH, chlorine, and chemical oxygen demand are significant for nallahs.
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