Multiple Linear Regression-Based Prediction Model to Detect Hexavalent Chromium in Drinking Water
This paper discusses the dependency between various water quality parameters (WQPs), namely pH, TDS, and conductivity that are determined to estimate the presence of hexavalent chromium compounds in drinking water. Multiple linear regression (MLR)-based prediction model is proposed to estimate the above parameters. The changes in WQPs are analyzed under both instant and stable conditions. The deviation between the measured and the estimated WQP is computed and added as the correction factor in order to improve the detection accuracy.
KeywordsWater quality parameters Hexavalent chromium Multiple linear regression Correction factor and detection accuracy
- 9.E C Alexopoulous, “Introduction to Multivariate Analysis”, Int. J. on Hippokartia, 14 (Suppl 1), 2010, 23–28.Google Scholar
- 10.Hana Vaskova, and Karel Koloznik, “Spectroscopic Measurement of trivalent and hexavalent chromium”, 17th Int. Carpathian Control Conference, 2016, 775–778.Google Scholar
- 11.S. Xu, X. Wang and C. Zhou, “A micro electrochemical sensor based on bismuth-modified mesoporous carbon for hexavalent chromium detection,” IEEE sensors conference, 2015, 1–4.Google Scholar
- 12.Y. Jiang and Z. Ma, “An assessment of water quality from a reach of Bailongjiang River, Gansu province,” 2011 Int. Conference on Electric Technology and Civil Engineering, 2011, 998–1000.Google Scholar
- 15.K. Sri Dhivya Krishnan and P.T.V. Bhuvaneswari, “Detection of Hexavalent Chromium contamination in drinking water using water quality sensor”, 9th Int. Conference on Trends in Industrial Measurements and Automation, 2017, 177–181.Google Scholar