基于变分模态分解-哈里斯鹰优化算法-核极限学习机模型的汛期日径流量预测

Daily runoff prediction during flood seasons based on the VMD-HHO-KELM model.

作者信息

Zhang Xianqi, Liu Fang, Yin Qiuwen, Wang Xin, Qi Yu

机构信息

Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China; Collaborative Innovation Center of Water Resources Efficient Utilization and Protection Engineering, Zhengzhou 450046, China; Technology Research Center of Water Conservancy and Marine Traffic Engineering, Zhengzhou, Henan Province 450046, China E-mail:

Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.

出版信息

Water Sci Technol. 2023 Jul;88(2):468-485. doi: 10.2166/wst.2023.227.

DOI:10.2166/wst.2023.227

PMID:37522446

Abstract

Improving the accuracy of daily runoff in the lower Yellow River is important for flood control and reservoir scheduling in the lower Yellow River. Influenced by factors such as meteorology, climate change, and human activities, runoff series present non-stationary and non-linear characteristics. To weaken the non-linearity and non-smoothness of runoff time series and improve the accuracy of daily runoff prediction, a new combined runoff prediction model (VMD-HHO-KELM) based on the ensemble Variational Modal Decomposition (VMD) algorithm and Harris Hawk Optimisation (HHO) algorithm-optimised Kernel Extreme Learning Machine (KELM) is proposed and applied to Gaocun and Lijin hydrological stations. The VMD-HHO-KELM model has the highest prediction accuracy, with the prediction model R reaching 0.95, mean absolute error reaching 13.3, and root mean square error reaching 33.83 at the Gaocun hydrological station, and R reaching 0.96, mean absolute error reaching 8.03, and root mean square error reaching 38.45 at the Lijin hydrological station.

摘要

提高黄河下游日径流量预测精度对黄河下游防洪及水库调度具有重要意义。受气象、气候变化和人类活动等因素影响，径流序列呈现出非平稳和非线性特征。为减弱径流时间序列的非线性和非光滑性，提高日径流量预测精度，提出了一种基于集成变分模态分解（VMD）算法和哈里斯鹰优化（HHO）算法优化的核极限学习机（KELM）的新型组合径流预测模型（VMD-HHO-KELM），并将其应用于高村和利津水文站。VMD-HHO-KELM模型具有最高的预测精度，在高村水文站预测模型R达到0.95，平均绝对误差达到13.3，均方根误差达到33.83；在利津水文站R达到0.96，平均绝对误差达到8.03，均方根误差达到38.45。