An application of multiple regression for predicting Turbidity of standard water quality for industrial and household consumption

Yusraini Muharni, Natalia Hartono


A multiple regression approach was applied in this study to predict the Turbidity value of standard water in a water treatment plant. Turbidity is a level of cloudiness in water due to the presence of particles or microorganisms. Turbidity in standard water did not affect human health in terms of hazardous, even though it represents poor quality water. Water treatment plants reduce the cloudiness in water by applying the chlorination process. There are three independent variables of water quality involved to predict turbidity value. They are PH, color-spectrum, and electrical conductivity. The correlation among variables was checked before conducting multiple regression. Color-spectrum has the highest correlation with turbidity. The stepwise approach remains two independent variables involved in multiple regression equation, color-spectrum and electrical conductivity with the value of   R-square equal to 0,97. Meaning that the two variables have the ability to explaining variations in turbidity up to 97 %. 


Quality Engineering

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M. Stevenson and C. Bravo, “Advanced turbidity prediction for operational water supply planning,” Decis. Support Syst., vol. 19, no. 4, pp. 72–84, 2019, doi: 10.1016/j.dss.2019.02.009.

Y. Zhang, X. Yao, Q. Wu, Y. Huang, Z. Zhou, J. Yang, and X. Liu, “Turbidity prediction of lake-type raw water using random forest model based on meteorological data: A case study of Tai lake, China,” Journal of Environmental Management., vol. 290, no. 2, pp. 112657, 2021, doi: 10.1016/j.jenvman.2021.112657.

S. Neupane, J. R. Vogel, D. E. Storm, B. J. Barfield, and A. R. Mittelstet, “Development of a turbidity prediction methodology for runoff–erosion models,” Water, Air, & Soil Pollution, vol. 226, no. 12, pp. 415, 2015, doi: 0.1007/s11270-015-2679-9.

C. Song and H. Zhang, “Study on turbidity prediction method of reservoirs based on long short term memory neural network,” Ecological Modelling, vol. 432, no. 12, pp. 109210, 2020, doi: 10.1016/j.ecolmodel.2020.109210.

C. Iglesias, J. M. Torres, P. J. G. Nieto, J. R. A. Fernández, C. D. Muñiz, J. I. Piñeiro, and J. Taboada, “Turbidity prediction in a river basin by using artificial neural networks: A case study in Northern Spain,” Water Resources Management, vol. 26, pp. 319–331, 2014, doi: 10.1007/s11269-013-0487-9.

J. Huang, R. Qian, J. Gao, H. Bing, Q. Huang, L. Qi, S. Song, and J. Huang, “A novel framework to predict water turbidity using Bayesian modeling,” Water Research, vol. 202, pp. 117406, 2021, doi: 10.1016/j.watres.2021.117406.

C. M. Kim and M. Parnichkun, “Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system,” Applied Water Science, vol. 7, pp. 3885–3902, 2017, doi: 10.1007/s13201-017-0541-5.

A. A. Scandinavica, S. B. Soil, E. Skarbøvik, and R. Roseth, “Use of sensor data for turbidity , pH and conductivity as an alternative to conventional water quality monitoring in four Norwegian case studies,” Acta Agric. Scand. Sect. B — Soil Plant Sci., vol. 65, no. 1, pp. 63–73, 2015, doi: 10.1080/09064710.2014.966751.

M. E. Bote and W. M. Desta, “Removal of turbidity from domestic wastewater using electrocoagulation: Optimization with response surface methodology,” Chemistry Africa, vol. 5, pp. 123–134, 2022, doi: 10.1007/s42250-021-00303-2.

H. Yao, W. Zhuang, Y. Qian, B. Xia, Y. Yang, and X. Qian, “Estimating and predicting metal concentration using online turbidity values and water quality models in two rivers of the Taihu basin, Eastern China,” PLoS ONE, vol. 11, no. 3, pp. e0152491, 2016, doi: 10.1371/journal.pone.0152491.

Y. Muharni, K. Kulsum, and A. Denisa, “Prediksi kualitas air baku dengan pendekatan Adaptive Neuro Fuzzy Inference System,” Semiinar Nas. IENACO 2018, pp. 602–606, 2018.

M. S. Gaya, M. U. Zango, L. A. Yusuf, M. Mustapha, and B. Muhammad, “Estimation of Turbidity in Water Treatment Plant using Hammerstein-Wiener and Neural Network Technique,” vol. 5, no. 3, pp. 666–672, 2017, doi: 10.11591/ijeecs.v5.i3.pp666-672.

I. G. Ezemagu, M. I. Ejimofor, M. C. Menkiti, and C. C. Nwobi-okoye, “Modeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural network,” South African J. Chem. Eng., vol. 35, pp. 78–88, 2021, doi: 10.1016/j.sajce.2020.11.007.

A. Mustapha and A. Abdu, “Application of Principal Component Analysis & Multiple Regression Models in Surface Water Quality Assessment,” Journal of Environment and Earth Science, vol. 2, no. 2, pp. 16–23, 2012.

M. W. Lechevallier, T. M. Evans, and R. J. Seidler, “Effect of Turbidity on Chlorination Efficiency and Bacterial Persistence in Drinking Water,” Appllied Environ. Microbiol., vol. 42, no. 1, pp. 159–167, 1981.

Harasit Kumar Mandal, “Influence of wastewater PH on Turbidity,” Int. J. Environ. Res. Dev., vol. 4, no. 2, pp. 105–114, 2015.

E.K. Read, V.P. Patil, S.K. Oliver, A.L. Hetherington, J.A. Brentrup, J.A. Zwart, K,M. Winters, J.R. Corman, E.R. Nodine, R.I. Wollway, H.A. Dugan, “The importance of lake-specific characteristics for water quality accorss the continental United States,” Ecological Apps., vol. 25, no. 4, p 943-955, 2015, doi: 10.1890/14-0935.1.

V. Garg, A.S. Kumar, S.P. Aggrawal, V. Kumar, P.R. Dhote, P.K. Thakur, B.R. Nikam, R.S. Sambare, A. Siddiqui, P.R. Muduli, G. Rastogi, “Spectral similarity approach for mapping turbidity of an inland waterbody,” J. of Hydrology, vol 550, p. 527-537, 2017, doi: 10.1016/j.jhydrol.2017.05.039.

A. Najah, A. Elshafie, O.A. Karim, O. Jaffar,”Prediction of Johor River water quality parameters using artificial neural networks,” European J. of Sci Research, vol 28, no. 3, p. 442-435, 2009.

Y. Wang, J. Chen, H. Cai, Q. Yu, and Z. Zhou,” Predicting water turbidity in a macro-tidal coastal bay using machine learning approaches,” Estuarine, Coastal and Shelf Science, vol 252, p. 107276, 2021, doi: 10.1016/j.ecss.2021.107276.



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