Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning
- Authors
- Kang, Hoon; Yang, Seunghyeok; Huang, Jianying; Oh, Jeill
- Issue Date
- Dec-2020
- Publisher
- INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
- Keywords
- Artificial intelligence; bidirectional LSTM; deep learning; neural net; prediction; rainfall; time series; wastewater treatment plant; water flow rate
- Citation
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.18, no.12, pp 3023 - 3030
- Pages
- 8
- Journal Title
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
- Volume
- 18
- Number
- 12
- Start Page
- 3023
- End Page
- 3030
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52496
- DOI
- 10.1007/s12555-019-0984-6
- ISSN
- 1598-6446
2005-4092
- 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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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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