International Journal of Control, Automation and Systems 2020; 18(12): 3023-3030
Published online December 17, 2020
https://doi.org/10.1007/s12555-019-0984-6
© The International Journal of Control, Automation, and Systems
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.
Download: http://link.springer.com/article/10.1007/s12555-019-0984-6
Keywords Artificial intelligence, bidirectional LSTM, deep learning, neural net, prediction, rainfall, time series, wastewater treatment plant, water flow rate.
International Journal of Control, Automation and Systems 2020; 18(12): 3023-3030
Published online December 1, 2020 https://doi.org/10.1007/s12555-019-0984-6
Copyright © The International Journal of Control, Automation, and Systems.
Hoon Kang*, Seunghyeok Yang, Jianying Huang, and JeiIl Oh
Chung-Ang University
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.
Download: http://link.springer.com/article/10.1007/s12555-019-0984-6
Keywords: Artificial intelligence, bidirectional LSTM, deep learning, neural net, prediction, rainfall, time series, wastewater treatment plant, water flow rate.
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