【摘要】 针对径流过程的非线性和非平稳性特点及预报精度低的问题，提出了结合时变滤波器的经验模态分解(TVF-EMD)、灰色关联度分析(GRA)和轻量级梯度提升机(LightGBM)的日径流预测组合模型。以黄河利津站和珠江高要站实测日径流序列为例，建立TVF-EMD-GRA-LightGBM(TGL)组合模型，并将其预测结果与多种单一或组合预测模型的预测结果进行了对比分析。结果表明：TGL组合模型高效且预测性能最佳，利津站和高要站日径流预测结果的纳什效率系数分别为0.949和0.966,相关系数分别为0.974和0.984,峰值流量预测误差分别小于0.078和0.073。TGL组合模型具有预测精度高、运行效率快、适用性强等优势，可用于日径流预测。更多还原

【Abstract】 In view of the nonlinear and non-stationary characteristics of runoff processes and low prediction accuracy, a daily runoff prediction model combining time-varying-filter-based empirical mode decomposition(TVF-EMD), grey relation analysis(GRA), and light gradient boosting machine(LightGBM) was proposed. Taking the measured daily runoff series of Lijin station on the Yellow River and Gaoyao station on the Pearl River as examples, the TVF-EMD-GRA-LightGBM(TGL) combined model was established, and its prediction results were compared with those of other single or combined models. The results show that the TGL combined model is efficient and has the best prediction performance, and the prediction results of the TGL combined model for Lijin and Gaoyao stations have the Nash-Sutcliffe efficiency coefficient of 0.95 and 0.97, respectively, the correlation coefficient of 0.97 and 0.98, respectively, and the peak flow prediction error less than 0.078 and 0.073, respectively. The TGL combined model has the advantages of high prediction accuracy, high operation efficiency, and strong applicability, providing a new way for daily runoff prediction and scientific regulation of water resources system.更多还原