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This study presents an explainable ensemble deep learning model for long-term streamflow forecasting, addressing uncertainties through a hierarchical optimization framework. The model incorporates a Dempster–Shafer evidence theory for predictor selection, an improved loss function for extreme value sensitivity, and a Stacking ensemble approach to enhance accuracy and interpretability. Results demonstrate significant improvements in forecasting accuracy and stability, highlighting the model

Author

summera khan

Language

EN