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Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

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Publicado en:Water vol. 16, no. 1 (2024), p. 69
Autor principal: Xie, Tao
Otros Autores: Chen, Lu, Yi, Bin, Li, Siming, Leng, Zhiyuan, Gan, Xiaoxue, Ziyi Mei
Publicado:
MDPI AG
Materias:
Feature selection
Hydrology
Accuracy
Algorithms
Decision trees
Time series
Forecasting
Watersheds
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2073-4441
DOI:10.3390/w16010069
Fuente:Publicly Available Content Database