人民珠江2023，Vol.44Issue(12)：64-72,79,10.DOI:10.3969/j.issn.1001-9235.2023.12.007
不同模态分解方法结合LSTM模型对日径流预报的影响
Impact of Different Mode Decomposition Methods Combined with LSTM Models on Daily Runoff Forecasting
摘要
Abstract
A combination of modal decomposition and deep learning forecasting methods was introduced to daily runoff forecasting to address the characteristics of unstable and volatile daily runoff series.Firstly,the complete ensemble empirical modal decomposition method was used to decompose the daily runoff time series,so as to obtain the modal components of different frequency components.Secondly,the daily runoff forecasting model was constructed for different modal components based on the long short-term memory neural network(LSTM),and the hyperparameters of the forecasting model were optimized using the grid search parametric optimization algorithm.Finally,the forecasting results of each model were modally reconstructed to obtain daily runoff forecasting results.The daily runoff forecasting of the Yichang hydrological station was taken as an example.Compared with the single LSTM,the RMSE,MAE,and MAPE of the proposed combination model were reduced by 65.02%,58.35%,and 2.88%,respectively.The decomposition effect of the complete ensemble empirical mode decomposition was better than that of the traditional modal decomposition method,which provided a new method and reference for nonlinear and non-stable daily runoff forecasting in a short time scale.关键词
日径流预报/长短期记忆网络/完整集合经验模态分解/网格搜索参数寻优算法/宜昌水文站Key words
daily runoff forecasting/long short-term memory neural network/complete ensemble empirical mode decomposition/grid search parametric optimization algorithm/Yichang hydrological station分类
建筑与水利引用本文复制引用
谭永杰,王现勋,段茗续,刘亚茹,姚华明..不同模态分解方法结合LSTM模型对日径流预报的影响[J].人民珠江,2023,44(12):64-72,79,10.基金项目
国家自然科学基金面上项目(51979198、91647204) （51979198、91647204）
