TL;DR
The case study for one-day-ahead forecasting indicates that the proposed framework achieves overall superior predictive performance relative to the benchmark methods according to six evaluation metrics and the Wilcoxon signed-rank test result, confirming the effectiveness of the proposed cross-attention fusion strategy and SHAP-guided input optimization.
The proposed framework integrates attention-enhanced recurrent modelling with physically informed heuristic constraints with physically informed heuristic constraints, allowing the model to capture complex nonlinear atmospheric dynamics while maintaining meteorological consistency.
The results indicated that the DTCN-KAN-MA model combined data-driven forecasting capabilities with improved predictive accuracy in the two studied basins, suggesting its potential utility for operational streamflow prediction in similar climatic settings.
Accurate and reliable streamflow forecasting is essential for hydropower generation, aquatic ecosystem management, and integrated water resource use. However, with ongoing climate change, streamflow series are increasingly exhibiting pronounced non-stationarity and non-linearity, which poses significant challenges fo...
The findings highlight the value of training strategies that allow models to directly learn bias correction during forecast transitions, emphasize the operational potential of combining sequential processing with near real-time discharge observations and identify physiographic catchment characteristics as key modulator...
This work advances scalable food security analytics by unifying statistical robustness with temporal dependency modelling, setting the stage for hybrid ensemble-GAT architectures in future research.
An explainable hybrid deep learning framework in which each module targets a specific forecasting challenge provides an accurate, interpretable, and scalable approach for monthly PWV forecasting over Türkiye.
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