摘要
针对地表降水量数据的非线性、非平稳特征,首先利用EEMD对月降水量初始数据进行分解,再利用Lempel-Ziv复杂度算法将分量划分为高频及低频分量,使用粒子群算法(PSO)优化基学习器参数,最终构建EEMD-SVR-ELM月降水量预测模型,并采用该模型对长江下游部分城市的月降水量实际数据进行预测。结果表明,该模型的综合性能最优,具有更高的精确度。相较于单一模型,在M_(MAE)、R_(RMSE)、M_(MAPE)指标上分别降低了37.4%、41.4%、42.5%,DM检验表明该模型显著优于其他模型,说明该模型可作为月降水量预测的一种有效新方法。
Aiming at the nonlinearity and non-stationary characteristics of surface precipitation data,a support vector regression(SVR)and extreme learning machine(ELM)are constructed as base learners.Firstly,the initial monthly precipitation data is decomposed based on Empirical Mode Decomposition(EEMD).Then the Lempel-Ziv complexity algorithm is used to divide the components into high-frequency and low-frequency components.The parameters of the base learner are optimized by particle swarm optimization(PSO).Finally,the EEMD-SVR-ELM monthly precipitation prediction model was constructed.Compared with other models,the model has the best comprehensive performance,higher accuracy and generalization.Especially compared with the single model,the M MAE,RRMSE,and M MAPE indicators were reduced by 37.4%,41.4%and 42.5%.The DM test showed that this model was significantly better than other models.This model can be used as an effective new method for monthly precipitation prediction.
作者
李明
刘东岳
赵良伟
蒋一波
LI Ming;LIU Dong-yue;ZHAO Liang-wei;JIANG Yi-bo(School of Business,Hohai University,Nanjing 211100,China;Institute of Project Management Informatization,Hohai University,Nanjing 211100,China;Jiangsu Huaiyin Water Conservancy Construction Co.,Ltd.,Huaian 223005,China)
出处
《水电能源科学》
北大核心
2024年第5期19-23,共5页
Water Resources and Power
基金
国家社会科学规划基金资助项目(17BGL156)
河海大学中央高校基本科研业务费项目(B220207039)。
