中文版 | English
Title

Deep learning-based prediction of effluent quality of a constructed wetland

Author
Corresponding AuthorFeng,Xiaochi
Publication Years
2023
DOI
Source Title
ISSN
2666-4984
EISSN
2666-4984
Volume13
Abstract
Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R. Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
Basic and Applied Basic Research Foundation of Guangdong Province[2019A1515010807];Harbin Institute of Technology[2021TS30];National Natural Science Foundation of China[51908161];National Natural Science Foundation of China[52100044];
WOS Research Area
Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS Subject
Green & Sustainable Science & Technology ; Environmental Sciences
WOS Accession No
WOS:000870518400001
Publisher
Scopus EID
2-s2.0-85139057006
Data Source
Scopus
Citation statistics
Cited Times [WOS]:2
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/406151
DepartmentSchool of Environmental Science and Engineering
Affiliation
1.State Key Laboratory of Urban Water Resource and Environment,School of Civil and Environmental Engineering,Harbin Institute of Technology (Shenzhen),Shenzhen,Guangdong,518055,China
2.Shenzhen Shenshui Water Resources Consulting CO,LTD,Shenzhen,Guangdong,518022,China
3.College of Biological Engineering,Beijing Polytechnic,Beijing,10076,China
4.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
Recommended Citation
GB/T 7714
Yang,Bowen,Xiao,Zijie,Meng,Qingjie,et al. Deep learning-based prediction of effluent quality of a constructed wetland[J]. Environmental Science and Ecotechnology,2023,13.
APA
Yang,Bowen.,Xiao,Zijie.,Meng,Qingjie.,Yuan,Yuan.,Wang,Wenqian.,...&Feng,Xiaochi.(2023).Deep learning-based prediction of effluent quality of a constructed wetland.Environmental Science and Ecotechnology,13.
MLA
Yang,Bowen,et al."Deep learning-based prediction of effluent quality of a constructed wetland".Environmental Science and Ecotechnology 13(2023).
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