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A Novel Deep Learning Ensemble Model Based on Two-stage Feature Selection and Intelligent Optimization for Water Quality Prediction by Wenli Liu; Tianxiang Liu; Zihan Liu; Hanbin Luo; Hanmin Pei is a Environmental Science article available to read on EtoBox.

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Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the op

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Author

Wenli Liu; Tianxiang Liu; Zihan Liu; Hanbin Luo; Hanmin Pei

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)