Sami, Balahaha Hadi Ziyad and Khai, Wong Jee and Sami, Balahaha Fadi Ziyad and Fai, Chow Ming and Essam, Yusuf and Ahmed, Ali Najah and El-Shafie, Ahmed (2021) Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction. Ain Shams Engineering Journal, 12 (2). pp. 1607-1622. ISSN 2090-4479, DOI https://doi.org/10.1016/j.asej.2021.01.007.
Full text not available from this repository.Abstract
This research studies the implementation of artificial neural networks (ANN) in predicting the concentration of total suspended solids (TSS) for the Fei Tsui reservoir in Taiwan. The prediction model developed in this study is designed to be used for monitoring the water quality in the Fei Tsui reservoir. High concentrations of total suspended solids (TSS) have been a crucial problem in the Fei Tsui reservoir for decades. As the Fei Tsui reservoir is a primary water source for Taipei City, this issue impacts the drinking water supply for the city due to etherification problems in the reservoir. 10-year average monthly records and 13-year average annual records have been collected for 26 parameters and correlated with the TSS concentrations to determine the parameters that have a strong relationship with the TSS concentrations. The parameters that were shown to have a strong correlation with the TSS concentration are the trophic state index (TSI), nitrate (NO3) concentration, total phosphorous (TP) concentration, iron concentration (IRON), and turbidity. Linear regression was used to develop the model that estimates the TSS concentration in the Fei Tsui Reservoir. The results show that model 3, a three-layer ANN model that uses three-input parameters namely NO3 concentration, TP concentration, and turbidity, with five neurons, to predict the output parameter which is TSS concentration, produces the highest coefficient of determination (R-2) and Willmott Index (WI), which are 0.9589 and 0.9933 respectively, and the lowest root mean square error, which is 0.4753. Based on these performance criteria, model 3 is concluded as the best model to predict TSS concentrations in this study. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
| Item Type: | Article |
|---|---|
| Funders: | Innovation & Research Management Center (iRMC), Universiti Tenaga Nasional (UNITEN), Malaysia from BOLD Journal Publication Fund 2021 [J5100D4103] |
| Uncontrolled Keywords: | Total suspended solids; Water quality; Machine learning algorithms; Time-series prediction and feitsui reservoir |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 25 Jul 2022 03:21 |
| Last Modified: | 25 Jul 2022 03:21 |
| URI: | http://eprints.um.edu.my/id/eprint/28721 |
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