摘要
受气象、气候变化和人类活动等因素的影响,径流序列呈现出非稳态、非线性等特征,给径流精准预测带来新的挑战。为了提高单一径流预测模型的预测精度,文章提出了一种新的基于集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)算法、变分模态分解(Variational Modal Decomposition,VMD)算法和麻雀搜索算法(Sparrow Search Algorithm,SSA)优化核极限学习机(Kernel Extreme Learning Machine,KELM)的组合径流预测模型(EEMD-VMD-SSA-KELM)。首先利用EEMD算法将径流序列分解为趋势分量、细节分量和随机分量,接着利用VMD算法将频率最大的随机分量进一步分解为若干个频率不同、较随机分量更加稳定的分量,从而降低径流序列的不稳定性;接着,对每个分量分别建立KELM模型进行预测,并采用SSA优化KELM模型的核参数和惩罚系数;最后,累加所有分量的预测结果得到径流序列的预测结果。提出的模型应用于湖北宜昌寸滩水文站的汛期日径流预测,并与BP神经网络模型、最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)模型、KELM模型等作对比。结果表明:组合了数据分解算法的预测模型的预测精度明显优于单一的BP模型、LSSVM模型和KELM模型;组合EEMD算法和VMD算法的预测模型的预测精度优于仅组合EEMD算法的预测模型;KELM模型的预测精度优于LSSVM模型;SSA的优化精度优于粒子群优化算法。EEMD-VMD-SSA-KELM模型的预测精度最高,能准确的模拟复杂多频信息的汛期日径流的变化趋势,可为水文预测及相关预测研究提供参考。
Influenced by such factors as meteorology,climate change and human activities,the runoff series present unsteady and nonlinear characteristics,which brings a new challenges to the accurate prediction of runoff.In order to improve the prediction accuracy of a single runoff prediction model,a new combined runoff prediction model(EEMD-VMD-SSA-KELM)based on ensemble empirical mode decomposition(EEMD)algorithm,variational modal decomposition(VMD)algorithm and kernel extreme learning machine(KELM)which is optimized by sparrow search algorithm(SSA)is proposed.Firstly,EEMD algorithm is used to decompose the runoff sequence into trend component,detail component and random component,and then VMD algorithm is used to further decompose the random component with the highest frequency into several components with different frequencies that are more stable than the random component to reduce the instability of the runoff sequence.Secondly,the KELM model is established for each component to predict,and SSA is used to optimize the kernel parameters and penalty coefficients of the KELM model.Finally,the prediction results of runoff series are obtained by accumulating the prediction results of all components.The proposed model is applied to the daily runoff prediction in the flood season of Cuntan Hydrological Station in Yichang,Hubei.The proposed model is applied to the flood season daily runoff prediction of Cuntan Hydrometric Station in Yichang,Hubei Province,and is compared with BP neural network model,Least Squares Support Vector Machine Model(LSSVM),KELM model,etc.The results show that the prediction accuracy of the model combined with data decomposition algorithm is obviously better than that of the single BP model,LSSVM model and KELM model,and the prediction accuracy of the model combined with EEMD algorithm and VMD algorithm was better than that of the model combined with EEMD algorithm only.The prediction accuracy of KELM model is better than that of LSSVM model;The optimization precision of SSA was better than that of particle swarm optimization(PSO)algorithm.The prediction accuracy of EEMD-VMD-SSA-KELM model is the highest,which can accurately simulate the change trend of daily runoff in flood season with complex multi frequency information,and can provide a reference for hydrological forecasting and related forecasting research.
作者
吴小涛
袁晓辉
袁艳斌
毛雅茜
肖加清
WU Xiao-tao;YUAN Xiao-hui;YUAN Yan-bin;MAO Ya-xi;XIAO Jia-Qing(College of Mathematics and Statistics,Huanggang Normal University,Huanggang 438000,Hubei Province,China;School of Civil and Hydraulic Engineering,Huazhong University of Science and Technology,Wuhan 430074,Hubei Province,China;Hubei Key Laboratory of Digital River Basin Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,Hubei Province,China;School of Resources and Environmental Engineering,Wuhan University of Technology,Wuhan 430070,Hubei Province,China)
出处
《中国农村水利水电》
北大核心
2023年第7期27-34,共8页
China Rural Water and Hydropower
基金
国家重点研发计划课题(2021YFC3200405,2021YFC3200305)
中国高校产学研创新基金(2021ITA03012)
湖北省教育厅科学技术研究项目(B2022196)
大学生创新创业训练计划项目(202110514136)。
