Accurate forecasting of dissolved oxygen (DO) is crucial for monitoring river water quality and protecting aquatic ecosystems. This study compares the performance of four deep learning models – Temporal Fusion Transformer (TFT), Informer, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) – for forecasting DO concentrations in the River Lee (London, UK) across 7- and 30-day time frames. A multivariate time-series dataset was employed, with temperature, turbidity, pH, conductivity, chlorophyll, and river flow as predictors. Model skills were evaluated using RMSE, MAE, R2, and SMAPE. Over the 7-day period, TFT had the lowest RMSE (0.06) and SMAPE (8.86%), while LSTM had the greatest R2 (0.77). TFT outperformed Informer, LSTM, and GRU at the 30-day horizon, with R2 = 0.79 and SMAPE of 8.23%, despite significant accuracy losses. According to the variable contribution study, temperature and river flow were the most significant factors, particularly for short-term projections. Overall, the results show that transformer-based structures, particularly TFT, can successfully represent nonlinear temporal dependencies and multivariate interactions, making them ideal for multi-horizon DO forecasting in river systems. These models have the ability to supplement normal monitoring by offering short-term predictions about probable oxygen conditions.
Ali et al. (Mon,) studied this question.