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Lookup NU author(s): Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
According to the world health organization, 485,000 people died each year due to water-related health diseases which are mainly contributed by poor river water quality. As a result, water quality monitoring stations have been deployed across the world. Unfortunately, due to the complex nature of the off-site water quality parameters, the water quality index (WQI) cannot be assessed in real-time. This has led to a significant push for the scientific community to develop an accurate and robust water quality prediction model. The dynamic and nonlinear nature of water quality parameters are major challenges for traditional machine learning algorithms such as multi linear regression to capture. In this study, the water quality index prediction model was developed using the feedforward artificial neural network (FANN) algorithms utilizing only on-site parameters. The performance of different nonlinear activation functions in the hidden neurons was thoroughly analysed which includes rectified linear unit (ReLU), scaled exponential linear unit (SELU), and exponential linear unit (ELU). Additionally, various initialization and optimization algorithms were also evaluated for maximum performance and efficiency. The results shows that FANN-ELU model coupled with Glorot initialization technique and AdaGrad optimizer outperformed other model combinations with an R2 value of 0.88 and mean squared error (MSE) value of 22.74.
Author(s): Djarum DH, Ahmad Z, Zhang J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 33rd Symposium of Malaysian Chemical Engineers (SOMChE 2022)
Year of Conference: 2022
Pages: 012022
Print publication date: 01/11/2022
Acceptance date: 12/07/2022
Date deposited: 16/11/2022
ISSN: 1757-8981
Publisher: IOP
URL: https://doi.org/10.1088/1757-899X/1257/1/012022
DOI: 10.1088/1757-899X/1257/1/012022
Series Title: IOP Conference Series: Materials Science and Engineering
