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<p>Rainfall input errors are a major source of uncertainty in flood forecasting, and merging multi-source precipitation data is essential for improving accuracy. Traditional merging methods often prioritize precipitation magnitude enhancements while overlooking event detection and false alarms. To address these limitations, this study developed a precipitation integration framework that combines machine learning classification-plus-regression models with Bayesian model averaging (BMA). Three machine learning algorithms—categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and random forest (RF)—were used to improve precipitation event detection. The framework includes spatial unification of raw satellite products using bilinear interpolation, bias correction through classification-plus-regression models, and final merging via a seasonal-scale BMA model. The method integrated GSMaP, IMERG, and PERSIANN satellite precipitation products, with ground observations used for model training (2001–2014) and independent validation (2015–2020) in the Upper Ganjiang River Basin, China. Results showed that the framework significantly enhanced precipitation estimation accuracy and detection capability. LightGBM-based integration exhibited superior detection performance (FAR = 0.08, CSI = 0.86), while RF-based integration achieved the highest overall accuracy (RMSE = 4.67, CC = 0.92). Seasonal variations in BMA weights underscored the need to account for seasonal characteristics of precipitation products. Additionally, accuracy improvements were observed across all rainfall categories, especially for heavy rainstorms. The seasonal-scale BMA fusion has combined the strengths of individual corrections and further enhanced precipitation estimation. This research offers a robust method for generating accurate rainfall inputs, providing valuable support for hydrological modeling and flood forecasting applications.</p>

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Seasonal machine learning fusion for improved satellite precipitation estimates: A case study in the upper Ganjiang River, China

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Yunyao Chen,
  • Binquan Li,
  • Yang Xiao,
  • Huiming Zhang,
  • Dong Xu,
  • Taotao Zhang,
  • Zhijun Wu

摘要

Rainfall input errors are a major source of uncertainty in flood forecasting, and merging multi-source precipitation data is essential for improving accuracy. Traditional merging methods often prioritize precipitation magnitude enhancements while overlooking event detection and false alarms. To address these limitations, this study developed a precipitation integration framework that combines machine learning classification-plus-regression models with Bayesian model averaging (BMA). Three machine learning algorithms—categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and random forest (RF)—were used to improve precipitation event detection. The framework includes spatial unification of raw satellite products using bilinear interpolation, bias correction through classification-plus-regression models, and final merging via a seasonal-scale BMA model. The method integrated GSMaP, IMERG, and PERSIANN satellite precipitation products, with ground observations used for model training (2001–2014) and independent validation (2015–2020) in the Upper Ganjiang River Basin, China. Results showed that the framework significantly enhanced precipitation estimation accuracy and detection capability. LightGBM-based integration exhibited superior detection performance (FAR = 0.08, CSI = 0.86), while RF-based integration achieved the highest overall accuracy (RMSE = 4.67, CC = 0.92). Seasonal variations in BMA weights underscored the need to account for seasonal characteristics of precipitation products. Additionally, accuracy improvements were observed across all rainfall categories, especially for heavy rainstorms. The seasonal-scale BMA fusion has combined the strengths of individual corrections and further enhanced precipitation estimation. This research offers a robust method for generating accurate rainfall inputs, providing valuable support for hydrological modeling and flood forecasting applications.