Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs

被引：3

作者：

Zhang, Juntao ^{[1
,2
]}

Zhang, Ziyue ^{[1
,2
]}

Weng, Ying ^{[1
,2
]}

Gosling, Simon ^{[1
,2
]}

Yang, Hui ^{[3
]}

Yang, Chenggang ^{[3
]}

Li, Wenjie ^{[3
]}

Ma, Qun ^{[3
]}

机构：

[1] Univ Nottingham, Ningbo Campus, Ningbo, Peoples R China

[2] Univ Nottingham, Nottingham Campus, Nottingham, England

[3] Ningbo Water Resources Bur, Ningbo, Peoples R China

来源：

2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020) | 2020年

关键词：

water resources; intelligent prediction; water level; Recurrent Neural Network (RNN); Artificial Neural Network (ANN); Long Short-Term Memory (LSTM);

D O I：

10.1109/COMPSAC48688.2020.0-108

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Water resources management over long term has faced a great challenge due to the increasing demands on water from a growing number of population and a huge variance of water usage in different time and place. Therefore, a new time series model based on Recurrent Neural Network (RNN), has been proposed and developed in this study for intelligent prediction of future water level in different reservoirs. We have carried out experiments on reservoirs in Ningbo, China, and the results have shown that our proposed model is more efficient on intelligent prediction of water level in reservoirs.

引用

页码：1125 / 1126

页数：2

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