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Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting.
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; Yi, Bin 1
; Li, Siming 1 ; Leng, Zhiyuan 1 ; Gan, Xiaoxue 1 ; Ziyi Mei 1 1 School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;
2 School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;