Monthly Precipitation Prediction Method Based on VMD-GA-BiLSTM
于霞 1宋杰 1段勇 1彭曦霆 1李冰洁1
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作者信息
1. 沈阳工业大学信息科学与工程学院,辽宁沈阳 110870
折叠
摘要
利用辽宁省气象局提供的地面观测降水资料,构建了具有多元时间特征的降水数据,采用变分模态分解方法(variational mode decomposition,VMD)组合遗传算法(genetic algorithm,GA)对双向长短时记忆神经网络(bidirectional long short-term memory,BiLSTM)进行优化,建立基于 VMD-GA-BiLSTM 的月降水量预测模型,并与BiLSTM、VMD-BiLSTM和GA-BiLSTM进行实验对比,应用均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)和R2决定系数作为模型评价指标.实验结果表明:VMD-GA-BiLSTM模型的R2 决定系数达到0.98,RMSE和MAE表现更低,验证了 VMD-GA-BiLSTM模型在时间序列预测方面的优势.
Abstract
Based on the ground observation precipitation data provided by Liaoning Meteorological Bureau,the precipitation data with multiple temporal characteristics were constructed,the VMD method and genetic algorithm(GA)were used to optimize the BiLSTM,and the monthly precipitation prediction model based on VMD-GA-BiLSTM was established,and the experimental comparison was carried out with BiLSTM,VMD-BiLSTM and GA-BiLSTM,and the determination coefficients of RMSE,MAE and R2 were used as the model evaluation indexes.Experimental results showed that the R2 determination coefficient of the VMD-GA-BiLSTM model reached 0.98,and the RMSE and MAE performance were lower,which verified the advantages of the VMD-GA-BiLSTM model in time series forecasting.
关键词
BiLSTM/VMD/遗传算法/月降水量/时序特征
Key words
BiLSTM/variational modal decomposition(VMD)/genetic algorithm/monthly precipitation/time-series features
维普
万方数据