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Article

Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

1
Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China
2
State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editors: Joong Hoon Kim and Donghwi Jung
Water 2021, 13(11), 1547; https://doi.org/10.3390/w13111547
Received: 12 May 2021 / Revised: 29 May 2021 / Accepted: 30 May 2021 / Published: 31 May 2021
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN. View Full-Text
Keywords: convolutional neural network (CNN); long short-term memory neural network (LSTM); complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN); real-time monitoring; water quality parameters convolutional neural network (CNN); long short-term memory neural network (LSTM); complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN); real-time monitoring; water quality parameters
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MDPI and ACS Style

Sha, J.; Li, X.; Zhang, M.; Wang, Z.-L. Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks. Water 2021, 13, 1547. https://doi.org/10.3390/w13111547

AMA Style

Sha J, Li X, Zhang M, Wang Z-L. Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks. Water. 2021; 13(11):1547. https://doi.org/10.3390/w13111547

Chicago/Turabian Style

Sha, Jian, Xue Li, Man Zhang, and Zhong-Liang Wang. 2021. "Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks" Water 13, no. 11: 1547. https://doi.org/10.3390/w13111547

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