Water Quality Index Prediction for Improvement of Treatment Processes on Drinking Water Treatment Plant

Goran Volf (), Ivana Sušanj Čule, Elvis Žic and Sonja Zorko
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Goran Volf: Department of Hydraulic Engineering, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia
Ivana Sušanj Čule: Department of Hydraulic Engineering, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia
Elvis Žic: Department of Hydraulic Engineering, Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia
Sonja Zorko: Istarski Vodovod d.o.o., 52420 Buzet, Croatia

Sustainability, 2022, vol. 14, issue 18, 1-16

Abstract: In order to improve the treatment processes of the drinking water treatment plant (DWTP) located near the Butoniga reservoir in Istria (Croatia), a prediction of the water quality index (WQI) was done. Based on parameters such as temperature, pH, turbidity, KMnO 4 , NH 4 , Mn, Al and Fe, the calculation of WQI was conducted, while for the WQI prediction models, along with the mentioned parameters, O 2 , TOC and UV254 were additionally used. Four models were built to predict WQI with a time step of one, five, ten, and fifteen days in advance, in order to improve treatment processes of the DWTP regarding the changes in raw water quality in the Butoniga reservoir. Therefore, obtained models can help in the optimization of treatment processes, which depend on the quality of raw water, and overall, in the sustainability of the treatment plant. Results showed that the obtained correlation coefficients for all models are relatively high and, as expected, decrease as the number of prediction days increases; conversely, the number of rules, and related linear equations, depends on the parameters set in the WEKA modelling software, which are set to default settings which give the highest values of correlation coefficient (R) for each model and the optimal number of rules. In addition, all models have high accuracy compared to the measured data, with a good prediction of the peak values. Therefore, the obtained models, through the prediction of WQI, can help to manage the treatment processes of the DWTP, which depend on the quality of raw water in the Butoniga reservoir.

Keywords: water quality index; prediction models; machine learning; water quality; treatment processes improvement; drinking water treatment plant; Butoniga reservoir (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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