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

Yang，Bowen1; Xiao，Zijie1; Meng，Qingjie2; Yuan，Yuan3; Wang，Wenqian1; Wang，Haoyu4; Wang，Yongmei1; Feng，Xiaochi1

2023

10.1016/j.ese.2022.100207

Environmental Science and Ecotechnology   Impact Factor & Quartile

2666-4984

2666-4984

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.

Constructed wetlands Deep learning LSTM Multi-source data fusion Water quality prediction

SCI

English

Others

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];

Science & Technology - Other Topics ; Environmental Sciences & Ecology

Green & Sustainable Science & Technology ; Environmental Sciences

WOS:000870518400001

ELSEVIER

2-s2.0-85139057006

Scopus

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

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.

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.

Yang，Bowen,et al."Deep learning-based prediction of effluent quality of a constructed wetland".Environmental Science and Ecotechnology 13(2023).