Control Theory and Applications

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

Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning

Hoon Kang*, Seunghyeok Yang, Jianying Huang, and JeiIl Oh

Chung-Ang University

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.

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.

Article

Control Theory and Applications

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.

Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning

Hoon Kang*, Seunghyeok Yang, Jianying Huang, and JeiIl Oh

Chung-Ang University

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.

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.

IJCAS
September 2023

Vol. 21, No. 9, pp. 2771~3126

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