改进时间卷积网络和长短时记忆网络的泸水河流域月径流量预测模型  

Monthly runoff prediction model of Lushui river basin based on improved TCN and LSTM

作　　者：王万良[1] 胡明志 张仁贡 董建杭 金雅文 WANG Wanliang;HU Mingzhi;ZHANG Rengong;DONG Jianhang;JIN Yawen(College of Computer Science and Technology,Zhejiang Uniersity of Technology,Hangzhou 310023,China;Zhejiang Yugong Information Technology Co.,Ltd.,Hangzhou 310052,China)

机构地区：[1]浙江工业大学计算机科学与技术学院,浙江杭州310023 [2]浙江禹贡信息科技有限公司,浙江杭州310052

出　　处：《计算机集成制造系统》2022年第11期3558-3575,共18页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(61873240)。

摘　　要：为提升水文模型预测精度和计算效率,解决传统方法难以浓缩多源水文特征,以及长时间跨度下数据量纲、值域不同造成的特征冗杂问题,提出基于改进时间卷积网络(TCN)和长短时记忆网络(LSTM)的月径流量预测模型。通过构造多卷积核并行网,以提取多源时序特征,并保持原因果卷积特性。引入扩张卷积抽取高阶水文特征,提升长时间跨度记忆单元处理效率。利用残差链接方式跨层传输底层完整特征,丰富特征结果,同时优化整体网络学习过程,并以泸水河流域为例进行验证。实验结果表明,该模型在计算效率、精度、网络结构上均优于其他对比模型,从而验证了其在该流域水文预测的有效性。As traditional methods are difficult to extract multi-source hydrological features and solve the problem of feature redundancy,a monthly runoff prediction model based on improved Temporal-Convolutional-Network(TCN)and Long Short Term Memory(LSTM)was proposed.The model constructed a multi-convolution kernel parallel network to extract multi-source timing features while maintaining the causal convolution characteristics.Dilated convolution was introduced to extract higher order hydrological features,improving the processing efficiency of memory units within a long period.The introduction of residual links enabled the complete features of the bottom stage to be transmitted across stages,which enriched the feature results and optimized the overall network structure,and Lushui River Basin was taken as an example for verification.The experimental results showed that the model was superior to other comparative models in terms of computational efficiency,accuracy and network structure,which verified the effectiveness of the model in hydrological prediction of the basin.

关 键 词：径流预测模型 时间卷积神经网络 长短时记忆神经网络 多源水文数据 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TV124[自动化与计算机技术—控制科学与工程]