Improving high-flow forecasting using dynamic multimodal feature fusion

This study evaluates a new approach to improving streamflow forecasting with deep learning: it focuses on the novel application of a dynamic multimodal feature fusion mechanism that adapts fusion operations based on the data's characteristics. Two baseline Long Short-Term Memory (LSTM) architectures are used, applying two dynamic fusion methods: dynamic operation-level fusion and attention-based fusion, to combine heterogeneous and multisource hydrometeorological data. The models are used for univariate (single flow gauge) and multivariate (multi-gauge) streamflow forecasting approaches. Applying these four approaches to the Severn Basin in the UK, known for long medium- to high-flow periods and shorter low-flow intervals, shows that the dynamic operation-level fusion consistently improved over the attention-based fusion in key performance metrics. In the multivariate case, Nash-Sutcliffe Efficiency (NSE) improved by 1.43%, Mean Absolute Error (MAE) decreased by 1.73%, Mean Absolute Scaled Error (MASE) dropped by 1.82%, and high-peak MAE decreased by 3.36%. For the univariate case, NSE improved by 1.44%, MAE decreased by 4.02%, MASE dropped by 3.89%, and high-peak MAE improved by 2.8%. In addition, multivariate models were considerably faster than univariate models, with training and inference times reduced by 74.57% and 73.81%, respectively. The multivariate models showed a 2.75% increase in NSE and a 72.04% decrease in MASE, indicating they captured better the hydrologic variability than the univariate models. Conversely, univariate models had a 20.59% lower MAE, a 21.17% lower high-peak MAE, and greater stability as indicated by tighter interquartile ranges, suggesting better error minimisation and more reliable predictions. Notably, in two river stations all models underperformed due to rapid flow variability and flashy hydrological responses in smaller catchment areas, suggesting in the future the use of higher-resolution climatic data. Overall, the study shows the potential of new dynamic multimodal fusion techniques, navigating the operational trade-offs between speed, stability, and accuracy across multi and uni-variate training strategies in streamflow forecasting. Nonetheless, the need for an optimal operational balance remains, suggesting further refinement of fusion techniques and focusing on minimising uncertainty.