Title: Monitoring and modelling of water quality parameters using artificial intelligence

Authors: Dayang P.M.A. Omar; Gasim Hayder; Yung-Tse Hung

Addresses: Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor Darul Ehsan, Malaysia ' Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (Uniten), 43000 Kajang, Selangor Darul Ehsan, Malaysia ' Department of Civil and Environmental Engineering, Cleveland State University, FH 112, 2121 Euclid Ave, Cleveland, Ohio, 44115, USA

Abstract: Rapid population growth leads to an increase in demand for water and spikes levels of water pollution. In this study, a low cost and innovative internet of things (IoT) device was used in the monitoring of water quality parameters. The monitoring system implemented used consists of maker-UNO as the core controller, SIM7600-GSM module as the Wi-Fi module and the water quality parameters sensors (total dissolved solids (TDS), oxidation reduction potential (ORP), temperature and turbidity). This study applied five different artificial intelligence (AI) techniques models to predict the water quality parameters. The data were collected from phytoremediation treatment system and modelled by using artificial neural network (ANN), regression trees, support vector machine (SVM), ensemble trees and the Gaussian process regression (GPR). A satisfying prediction models were achieved indicating that early prevention of contamination in the treatment system can be achieved through the application of monitoring and artificial intelligence modelling tools.

Keywords: monitoring; water quality; prediction model; artificial intelligence.

DOI: 10.1504/IJEWM.2023.131153

International Journal of Environment and Waste Management, 2023 Vol.31 No.4, pp.525 - 533

Received: 04 Apr 2020
Accepted: 19 Sep 2020
Published online: 01 Jun 2023 *

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