Regular Papers

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

Regular Papers

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
February 2024

Vol. 22, No. 2, pp. 347~729

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