咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >A water quality prediction mod... 收藏

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China

作     者:Song, Chenguang Yao, Leihua Hua, Chengya Ni, Qihang 

作者机构:China Univ Geosci Beijing Sch Engn & Technol Beijing 100083 Peoples R China 

出 版 物:《ENVIRONMENTAL MONITORING AND ASSESSMENT》 (环境监测与评价)

年 卷 期:2021年第193卷第6期

页      面:363-363页

核心收录:

学科分类:0830[工学-环境科学与工程(可授工学、理学、农学学位)] 08[工学] 

基  金:The authors would like to thank the editors and anonymous reviewers for their constructive comments that greatly contributed to improving the manuscript 

主  题:VMD SSA LSSVM Water quality prediction Optimization algorithm Hybrid model 

摘      要:Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation. To overcome nonstationarity, randomness, and nonlinearity of the water quality parameter data, an improved least squares support vector machine (LSSVM) model was proposed to improve the model s performance at two gaging stations, namely Panzhihua and Jiujiang, in the Yangtze River, China. In addition, a hybrid model that recruits variational mode decomposition (VMD) to denoise the input data was adopted. A novel metaheuristic optimization algorithm, the sparrow search algorithm (SSA) was also implemented to compute the optimal parameter values for the LSSVM model. To validate the proposed hybrid model, standalone LSSVM, SSA-LSSVM, VMD-LSSVM, support vector regression (SVR), as well as back propagation neural network (BPNN) were considered as the benchmark models. The results indicated that the VMD-SSA-LSSVM model exhibited the best forecasting performance among all the peer models at Panzhihua station. Furthermore, the model forecasting results applied at Jiujiang were consistent with those at Panzhihua station. This result further verified the accuracy and stability of the VMD-SSA-LSSVM model. Thus, the proposed hybrid model was effective method for forecasting nonstationary and nonlinear water quality parameter series and can be recommended as a promising model for water quality parameter forecasting.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分