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

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

Abstract

© 2020 IEEE. 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.

Citation

Zhang, J., Zhang, Z., Weng, Y., Gosling, S., Yang, H., Yang, C., Li, W., & Ma, Q. (2020, January). Using Recurrent Neural Network for Intelligent Prediction of Water Level in Reservoirs. Presented at Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020, Madrid, Spain

• Conference Name: Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
• Start Date: Jan 13, 2020
• End Date: Jul 17, 2020
• Online Publication Date: Sep 22, 2020
• Publication Date: Jul 17, 2020
• Deposit Date: Jan 8, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1125-1126
• ISBN: 9781728173030
• DOI: https://doi.org/10.1109/COMPSAC48688.2020.0-108
• Public URL: https://nottingham-repository.worktribe.com/output/5029226
• Publisher URL: https://ieeexplore.ieee.org/document/9202467