计算机与数字工程2024，Vol.52Issue(4)：1149-1153,5.DOI:10.3969/j.issn.1672-9722.2024.04.033
基于VMD-PSO-LSSVM的降雨量预测研究
Research on Rainfall Prediction Based on VMD-PSO-LSSVM
摘要
Abstract
Rainfall events are highly random.In order to scientifically and effectively predict complex rainfall,a rainfall pre-diction model based on VMD-PSO-LSSVM is proposed.Firstly,VMD method is used to decompose the original rainfall series.Then,particle swarm optimization is used to optimize the key parameters of least squares support vector machine,and a series of subsequences are predicted by accurately constructed prediction model.Finally,all prediction subsequences are synthesized to ob-tain the final prediction results.The simulation results show that the prediction results of VMD-PSO-LSSVM model have less error and higher accuracy.It can become an effective rainfall prediction tool,provide reference for agriculture and water conservancy de-partments to make water resources management decisions,and reduce the risk of drought and flood disasters.关键词
变分模态分解/粒子群算法/最小二乘支持向量机/降雨量Key words
variational modal decomposition/particle swarm optimization/least squares support vector machine/rainfall分类
数理科学引用本文复制引用
申杨,王文波..基于VMD-PSO-LSSVM的降雨量预测研究[J].计算机与数字工程,2024,52(4):1149-1153,5.
