[1]郭宝椿,李佐勇,陈健,等.融合长短时记忆与图结构学习的水库水位预测[J].福建理工大学学报,2024,22(01):90-94.[doi:10.3969/j.issn.2097-3853.2024.01.013]
　GUO Baochun,LI Zuoyong,CHEN Jian,et al.Reservoir level prediction via integrating long short-term memory and graph structure learning[J].Journal of Fujian University of Technology;,2024,22(01):90-94.[doi:10.3969/j.issn.2097-3853.2024.01.013]
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融合长短时记忆与图结构学习的水库水位预测()
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《福建理工大学学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第22卷
期数:
2024年01期
页码:
90-94
栏目:
出版日期:
2024-02-25

文章信息/Info

Title:
Reservoir level prediction via integrating long short-term memory and graph structure learning
作者:
郭宝椿; 李佐勇; 陈健; 卢维楷; 马森标
福建理工大学电子电气与物理学院
Author(s):
GUO Baochun;  LI Zuoyong;  CHEN Jian;  LU Weikai;  MA Senbiao
School of Electronic, Electrical and Physics, Fujian University of Technology
关键词:
水库水位预测; 长短期记忆网络; 图神经网络; 深度学习
Keywords:
reservoir level prediction;  long short-term memory network;  graph neural network;  deep learning
分类号:
TP389.1
DOI:
10.3969/j.issn.2097-3853.2024.01.013
文献标志码:
A
摘要:
水库水位变化受降雨、泄洪、蒸发等众多因素影响,现有水库水位预测方法的预测精度有待提升。为此,提出一种融合长短期记忆网络( long short-term memory,LSTM) 和图卷积神经网络( graphconvolution neural network,GCN)的水库水位预测模型。该模型首先借助LSTM 提取水位与相关影响因素的时序依赖特征;随后,设计图结构学习模块,自动捕捉水位及不同影响因素间的关联关系;最后利用GCN 进行表征学习和预测。在三峡大坝数据集及合作企业提供的数据集上开展了广泛实验,实验结果证实了所提模型的有效性和优越性。
Abstract:
The water level change of reservoirs is affected by many factors such as rainfall, flood discharge, and evaporation. The prediction accuracy of existing reservoir water level prediction methods needs to be improved. Therefore, a reservoir water level prediction model was proposed integrating long short-term memory (LSTM) and graph convolution neural network (GCN). The proposed model first extracts time-series dependent features of water level and related influencing factors by using LSTM. Then, a graph structure learning module is designed to automatically capture the correlation between water level and different influencing factors. Finally, GCN is used for feature learning and prediction. Extensive experiments were conducted on the Three Gorges Dam dataset and datasets provided by cooperative enterprises. The experimental results demonstrated the effectiveness and superiority of the proposed model.

参考文献/References:

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更新日期/Last Update: 2024-02-25