Time series trend modelling and forecasting of selected water quality parameters in the Mthatha River Catchment, South Africa. Issue 1 (10th January 2023)

• Record Type: Journal Article
• Authors: Amoo, Oseni Taiwo; Abayomi, Abdultaofeek; Ikudayisi, Akinola; Makupula, Nombuyiselo
• Is Part Of: Water practice and technology. Volume 18:Issue 1(2023)
• Page Start: 185
• Page End: 200
• Publication Date: 2023-01-10
• Subjects: Mann–Kendall -- pollutants -- river chemistry -- stochastic model -- water quality; Sewerage; Sewerage -- Management; Water-supply; Water-supply engineering; Periodicals; 628.205
• DOI: 10.2166/wpt.2023.002
• Languages: English
• ISSNs: 1751-231X

Abstract

Abstract: Over recent decades, water quality at the Mthatha River Catchment (MRC) within the Eastern Cape Province of South Africa has been threatened by various pollutants. The continuous effluent concentration discharges from the Mthatha Prison and the Efata School for the Blind and Deaf have caused ineffable damage to the Mthatha River's water quality. Thus, the time series-measured data between 2012 and 2020 were analysed to determine the trends and enable forecasting of selected water quality parameters using the Thomas–Fiering (T–F) stochastic model. The Kendall's τ test trends show an increase in the coefficient of variation of 0.498 and 0.394 at the Mthatha Prison and Efata School, respectively, for abrupt changes, whereas the mean monthly T–F forecasted model shows a good correlation value range from 0.79 to 0.87 for the various predicted variables. The simulated predicted models and trends could serve as a measure to forecast selected water quality parameters' occurrence and a likelihood period when the river pollutants could be controlled. Water managers and researchers would find usefulness in the employed tools for an effective control planning of the river pollutants. HIGHLIGHTS: Innovative ways for analysing and forecasting water quality (WQ) parameters. Prerequisite for prompt mitigation measures in maintaining a healthy river state. Contribution to the statistical approach, visual modelling, and predictive algorithms.