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

被引：0

作者：

Wang W. ^{[1
]}

Hu M. ^{[1
]}

Zhang R. ^{[2
]}

Dong J. ^{[1
]}

Jin Y. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Zhejiang Uniersity of Technology, Hangzhou

[2] Zhejiang Yugong Information Technology Co., Ltd., Hangzhou

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 11期

基金：

中国国家自然科学基金;

关键词：

long and short term memory neural network; multi-source hydrological data; runoff forecast model; time convolutional neural network;

D O I：

10.13196/j.cims.2022.11.019

中图分类号：

学科分类号：

摘要：

As traditional methods are difficult to extract multi-source hydro logical features and solve the problem of feature redundancy, a monthly runoff prediction model based on improved Temporal-Convolutional-Nelwork (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 hydro logical 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. © 2022 CIMS. All rights reserved.

引用

页码：3558 / 3575

页数：17

共 66 条

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