Monitoring and modelling of water quality parameters using artificial intelligence

• Main Authors: Omar D.P.M.A., Hayder G., Hung Y.-T.
• Other Authors: 58313272000
• Format: Article
• Published: Inderscience Publishers 2024
• Subjects: artificial intelligence; monitoring; prediction model; water quality; Bioremediation; Forecasting; Global system for mobile communications; Internet of things; Neural networks; Population statistics; Redox reactions; Support vector machines; Water pollution; Low-costs; Model of water quality; Monitoring system; Oxidation-reduction potentials; Prediction modelling; Rapid population growth; Solid oxidation; Total dissolved solids; Treatment systems; Water quality parameters; Water quality

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. Copyright � 2023 Inderscience Enterprises Ltd.