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<p>Runoff forecasting precision is critical for water resource management and watershed ecological operation. However, robust runoff prediction is difficult because of runoff series' instability and nonlinearity. To address these challenges, this study develops an innovative ensemble forecasting framework that integrates decomposition-reconstruction techniques and a weight combination strategy to enhance both point and interval forecasting. Specifically, we propose a hybrid point-interval forecasting framework that leverages commonly used distribution functions to quantify uncertainty and generate high-quality runoff prediction intervals. Compared with traditional methods, the proposed framework improves predictive accuracy by optimizing the combination of multiple forecasting models and reducing error accumulation through adaptive sequence reconstruction. To evaluate the effectiveness of the proposed approach, we conducted a comparative analysis across multiple watersheds. Outcomes indicate that: (1) The proposed point forecasting framework outperforms the benchmark models (e.g., Mean Absolute Percentage Error (MAPE) ≤ 0.1494); (2) By incorporating decomposition-reconstruction technology, our framework efficiently captures runoff characteristics, thereby enhancing forecasting performance; (3) The constructed interval forecasting framework effectively generates accurate forecasting intervals, achieving a minimum Prediction Interval Coverage Probability (PICP) value of 0.8750 on the monthly scale and 0.9676 on the daily scale. These findings highlight the effectiveness of the proposed hybrid framework as a powerful tool for water resource planning and decision-making.</p>

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Ensemble Framework for Multi-scale Runoff Interval Forecasting using Weight Combination and Reconstruction Strategy

  • 期刊论文

  • 发表日期：

    2025年3月6日

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  • Xi Yang,
  • Min Qin,
  • Zhihua Zhu,
  • Zhihe Chen

摘要

Runoff forecasting precision is critical for water resource management and watershed ecological operation. However, robust runoff prediction is difficult because of runoff series' instability and nonlinearity. To address these challenges, this study develops an innovative ensemble forecasting framework that integrates decomposition-reconstruction techniques and a weight combination strategy to enhance both point and interval forecasting. Specifically, we propose a hybrid point-interval forecasting framework that leverages commonly used distribution functions to quantify uncertainty and generate high-quality runoff prediction intervals. Compared with traditional methods, the proposed framework improves predictive accuracy by optimizing the combination of multiple forecasting models and reducing error accumulation through adaptive sequence reconstruction. To evaluate the effectiveness of the proposed approach, we conducted a comparative analysis across multiple watersheds. Outcomes indicate that: (1) The proposed point forecasting framework outperforms the benchmark models (e.g., Mean Absolute Percentage Error (MAPE) ≤ 0.1494); (2) By incorporating decomposition-reconstruction technology, our framework efficiently captures runoff characteristics, thereby enhancing forecasting performance; (3) The constructed interval forecasting framework effectively generates accurate forecasting intervals, achieving a minimum Prediction Interval Coverage Probability (PICP) value of 0.8750 on the monthly scale and 0.9676 on the daily scale. These findings highlight the effectiveness of the proposed hybrid framework as a powerful tool for water resource planning and decision-making.