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

Abstract

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.

Climate Change Impacts on Viticulture in Italy: Insights from Historical and Future Scenarios Across Administrative Areas, Latitudes, and Elevations

“…To ensure the reliability of the results and as suggested by several authors [38][39][40][41], relying on a single GCM may be misleading or lead to an overestimation of the data. In our case, the 13 GCMs available on worldclim.org were considered simultaneously by creating an Ensemble dataset.…”