@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",
}