Multi-Step Dissolved Oxygen Forecasting and Driver Identification in the Yangtze River Basin Using VMD-Bayes-LSTM and SHAP

Dissolved oxygen (DO) dynamics in river systems exhibit complex nonlinear and non-stationary characteristics driven by interactions among meteorological and water-quality factors, posing challenges for accurate prediction and identification of dominant drivers. To address these challenges, this study establishes a VMD-Bayes-LSTM model by integrating Variational Mode Decomposition (VMD), Bayesian optimization, and Long Short-Term Memory (LSTM) networks. The datasets used in this study were collected from monitoring data at eight sites in the Yangtze River Basin, including water-quality and meteorological factors. Compared with several benchmark models, the VMD-Bayes-LSTM model achieves the best performance in daily DO concentration prediction, with mean R2, KGE, MSE, and MAE values of 0.876, 0.900, 0.208, and 0.247, respectively. Meanwhile, this model supports multi-step prediction and achieves five-day-ahead forecasting of DO concentrations. Based on this, a SHAP value-interpretable VMD-Bayes-LSTM model is constructed. Analysis indicates that water temperature and average air temperature are the primary predictive factors at the CC, PT, DQ, LS, NJG, and ZT sites with a cumulative contribution rate exceeding 60%, reaching a maximum of 90% at the PT site; notably, at the LD and LJG sites, turbidity emerges as the dominant water-quality parameter, accounting for 59.6% and 36.2% of the predictive contribution, respectively.