Abstract
This paper not only addresses a feasible strategy in predicting time series or sequences by using deep neural nets such as bi-LSTM (bidirectional Long Short-Term Memory), but also demonstrates fairly good results of forecasting wastewater flow rate for a municipal wastewater treatment plant in a practical sense. The basic procedures of time series prediction by deep learning are to collect the past information of all available states for deep learning and to utilize p-step ahead delays of a no-training interval with a sliding time window. Therefore, the sequence-to-point p-step prediction of sewage flow of Yangju wastewater treatment plant could be made possible by using bi-LSTM in accordance with this fundamental principle.
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Recommended by Associate Editor DaeEun Kim under the direction of Editor Euntai Kim. This research was supported by Korea Environment Industry & Technology Institute (KEITI) through Public Technology Program based on Environmental Policy (Project No.: 2018000700005), funded by Korea Ministry of Environment (MOE), and by the Chung-Ang University Graduate Research Scholarship in 2020.
Hoon Kang received his B.S. and M.S. degrees in electronic engineering from the Seoul National University, Korea, in 1982 and 1984, respectively. He earned a Ph.D. degree in the School of Electrical Engineering at the Georgia Institute of Technology, Atlanta, in 1989. From 1989 to 1991, he was first a postdoctoral fellow and then a research associate in the Georgia Tech Electrical Engineering Department. Since 1992, he joined the School of Electrical and Electronics Engineering at Chung-Ang University, Seoul, Korea. He has served as the financial secretary for the institutes such as KIIS, ICROS, and IEIE. With IEIE, he was an editorial board member and a general affairs director. His research interests include computational intelligence such as deep learning, machine learning, fuzzy logic systems, and robot vision.
Seunghyeok Yang received his B.S. degree from the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea, in 2020. He is currently studying for an M.S. degree in the Department of Electrical and Electronics Engineering, Chung-Ang University. His areas of interest includes GAN based on deep learning and computer vision.
Jianying Huang received his Bachelor’s degree in the School of Electrical and Electronic Engineering, Chung-Ang University in Seoul, Korea in 2019. He is currently studying for an M.S. degree in the Department of Electrical and Electronic Engineering at Chung-Ang University in Seoul, South Korea. His current research areas are deep learning applications using Bi-LSTM to predict time series.
Jeill Oh is currently a professor in the Department of Civil and Environmental Engineering at Chung-Ang University. He received a B.S. degree from the Yonsei University, Korea in 1992, and his M.S. and Ph.D. degrees in Civil and Environmental Engineering from the University of Colorado at Boulder, USA, in 1995 and 1998, respectively. He worked as a post-doctoral research fellow in the School of Environmental Engineering in Pohang University of Science and Technology (POSTECH) prior to joining Chung-Ang in 2000. He has been serving as a member of the Executive Committee of Korean Society of Water & Wastewater (KSWW), and Korean Society of Water Environment (KSWE) for fifteen years. He is interested in diverse aspects of ICT(nformation and Communication Technology) coupled with environmental system such as real-time monitoring (RTM), real-time control (RTC), optimization and deep leaning technology in urban water system.
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Kang, H., Yang, S., Huang, J. et al. Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning. Int. J. Control Autom. Syst. 18, 3023–3030 (2020). https://doi.org/10.1007/s12555-019-0984-6
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DOI: https://doi.org/10.1007/s12555-019-0984-6