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

Juntao Zhang; Ziyue Zhang; Ying Weng; Simon Gosling; Hui Yang; Chenggang Yang; Wenjie Li; Qun Ma

doi:10.1109/COMPSAC48688.2020.0-108

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

Juntao Zhang
, Ziyue Zhang
, Ying Weng
, Simon Gosling
, Hui Yang
, Chenggang Yang
, Wenjie Li
, Qun Ma

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
Editors	W. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1125-1126
Number of pages	2
ISBN (Electronic)	9781728173030
DOIs	https://doi.org/10.1109/COMPSAC48688.2020.0-108
Publication status	Published - Jul 2020
Event	44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 - Virtual, Madrid, Spain Duration: 13 Jul 2020 → 17 Jul 2020

Publication series

Name	Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020

Conference

Conference	44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020
Country/Territory	Spain
City	Virtual, Madrid
Period	13/07/20 → 17/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation

Free Keywords

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

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Software
Education

Access to Document

10.1109/COMPSAC48688.2020.0-108

Cite this

Zhang, J., Zhang, Z., Weng, Y., Gosling, S., Yang, H., Yang, C., Li, W., & Ma, Q. (2020). Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs. In W. K. Chan, B. Claycomb, H. Takakura, J.-J. Yang, Y. Teranishi, D. Towey, S. Segura, H. Shahriar, S. Reisman, & S. I. Ahamed (Eds.), Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020 (pp. 1125-1126). Article 9202467 (Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/COMPSAC48688.2020.0-108

Zhang, Juntao ; Zhang, Ziyue ; Weng, Ying et al. / Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs. Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020. editor / W. K. Chan ; Bill Claycomb ; Hiroki Takakura ; Ji-Jiang Yang ; Yuuichi Teranishi ; Dave Towey ; Sergio Segura ; Hossain Shahriar ; Sorel Reisman ; Sheikh Iqbal Ahamed. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1125-1126 (Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020).

@inproceedings{732ea5b7a2d1470ab4abbe2ddb77aa0c,

title = "Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs",

abstract = "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.",

keywords = "Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), intelligent prediction, water level, water resources",

author = "Juntao Zhang and Ziyue Zhang and Ying Weng and Simon Gosling and Hui Yang and Chenggang Yang and Wenjie Li and Qun Ma",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 ; Conference date: 13-07-2020 Through 17-07-2020",

year = "2020",

month = jul,

doi = "10.1109/COMPSAC48688.2020.0-108",

language = "English",

series = "Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1125--1126",

editor = "Chan, \{W. K.\} and Bill Claycomb and Hiroki Takakura and Ji-Jiang Yang and Yuuichi Teranishi and Dave Towey and Sergio Segura and Hossain Shahriar and Sorel Reisman and Ahamed, \{Sheikh Iqbal\}",

booktitle = "Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020",

address = "United States",

}

Zhang, J, Zhang, Z, Weng, Y, Gosling, S, Yang, H, Yang, C, Li, W & Ma, Q 2020, Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs. in WK Chan, B Claycomb, H Takakura, J-J Yang, Y Teranishi, D Towey, S Segura, H Shahriar, S Reisman & SI Ahamed (eds), Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020., 9202467, Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1125-1126, 44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020, Virtual, Madrid, Spain, 13/07/20. https://doi.org/10.1109/COMPSAC48688.2020.0-108

Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs. / Zhang, Juntao; Zhang, Ziyue; Weng, Ying et al.
Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020. ed. / W. K. Chan; Bill Claycomb; Hiroki Takakura; Ji-Jiang Yang; Yuuichi Teranishi; Dave Towey; Sergio Segura; Hossain Shahriar; Sorel Reisman; Sheikh Iqbal Ahamed. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1125-1126 9202467 (Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs

AU - Zhang, Juntao

AU - Zhang, Ziyue

AU - Weng, Ying

AU - Gosling, Simon

AU - Yang, Hui

AU - Yang, Chenggang

AU - Li, Wenjie

AU - Ma, Qun

PY - 2020/7

Y1 - 2020/7

N2 - 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.

AB - 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.

KW - Artificial Neural Network (ANN)

KW - Long Short-Term Memory (LSTM)

KW - Recurrent Neural Network (RNN)

KW - intelligent prediction

KW - water level

KW - water resources

UR - https://www.scopus.com/pages/publications/85094158454

U2 - 10.1109/COMPSAC48688.2020.0-108

DO - 10.1109/COMPSAC48688.2020.0-108

M3 - Conference contribution

AN - SCOPUS:85094158454

T3 - Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020

SP - 1125

EP - 1126

BT - Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020

A2 - Chan, W. K.

A2 - Claycomb, Bill

A2 - Takakura, Hiroki

A2 - Yang, Ji-Jiang

A2 - Teranishi, Yuuichi

A2 - Towey, Dave

A2 - Segura, Sergio

A2 - Shahriar, Hossain

A2 - Reisman, Sorel

A2 - Ahamed, Sheikh Iqbal

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020

Y2 - 13 July 2020 through 17 July 2020

ER -

Zhang J, Zhang Z, Weng Y, Gosling S, Yang H, Yang C et al. Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs. In Chan WK, Claycomb B, Takakura H, Yang JJ, Teranishi Y, Towey D, Segura S, Shahriar H, Reisman S, Ahamed SI, editors, Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1125-1126. 9202467. (Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020). doi: 10.1109/COMPSAC48688.2020.0-108

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

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this