人民长江2024，Vol.55Issue(6)：129-135,7.DOI:10.16232/j.cnki.1001-4179.2024.06.018
基于注意力机制与LSTM-CCN的月降水量预测
Monthly precipitation prediction based on attention mechanism and LSTM-CCN
摘要
Abstract
To address the issue of low accuracy in existing monthly precipitation prediction methods,an attention mechanism and LSTM-CCN for the monthly precipitation prediction method were proposed.Firstly,the long short-term memory neural net-work(LSTM)was used to extract the temporal feature distribution of meteorological data,capturing the statistical distribution in adjacent or long-distance meteorological data segments from a temporal correlation perspective.Secondly,the causal convolution-al network(CCN)projected meteorological data to the spatial dimension,deeply capturing the statistical distribution of spatial features of meteorological data.Thirdly,the time and space features were input into the cross-attention network in parallel,con-structing a fused spatiotemporal feature.Finally,a decoder constructed with the long short-term memory neural network took the fused spatiotemporal feature as input,and the predicted monthly precipitation served as the output.The test was carried out on the data set from Xinxiang City,Henan Province from 2001 to 2017.The results showed that the proposed method's root mean square error was only 13.08 mm,demonstrating lower prediction errors compared to mainstream methods.The introduction of this work contributes to enhancing the accuracy and practicality of meteorological predictions.关键词
月降水量预测/多层注意力机制/因果卷积神经网络/长短时记忆神经网络Key words
monthly precipitation prediction/multi-layer attention mechanism/causal convolutional neural network/long short-term memory neural network分类
建筑与水利引用本文复制引用
周祥,张世明,苏林鹏,张守平..基于注意力机制与LSTM-CCN的月降水量预测[J].人民长江,2024,55(6):129-135,7.基金项目
重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0132) （CSTB2022TIAD-KPX0132）
