基于VMD-PSO-LSSVM的降雨量预测研究

Research on Rainfall Prediction Based on VMD-PSO-LSSVM

降雨事件具有高度随机性,为了对复杂的降雨量进行科学有效地预测,提出VMD-PSO-LSSVM的降雨量预测模型。首先利用VMD方法分解原始降雨量序列;然后运用粒子群算法优化最小二乘支持向量机的关键参数,使用精准构建的预测模型对一系列子序列进行预测;最后合成所有的预测子序列,获得最终预测结果。仿真结果表明,VMD-PSO-LSSVM模型预测结果误差更小,准确度更高,可以成为有效的降雨量预测工具,为农业和水利部门制定水资源管理决策提供参考,降低旱涝灾害的风险。

Rainfall events are highly random.In order to scientifically and effectively predict complex rainfall,a rainfall prediction 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 obtain 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 departments to make water resources management decisions,and reduce the risk of drought and flood disasters.