Open AccessElectrical Conductivity as a General Predictor of Multiple Parameters in Tigris River Based on Statistical Regression Model
Author(s) -
Omar Anmar Khlaif,
Khalid Adel Abdulrazzaq,
Abbas Mohammed
Publication year - 2021
Publication title -
maǧallaẗ al-handasaẗ/journal of engineering
Language(s) - English
Resource type - Journals
eISSN - 2520-3339
pISSN - 1726-4073
DOI - 10.31026/j.eng.2021.02.06
Subject(s) - linear regression , rule of thumb , regression analysis , statistics , total dissolved solids , electrical resistivity and conductivity , regression , water quality , conductivity , chloride , mathematics , soil science , environmental science , materials science , environmental engineering , chemistry , metallurgy , engineering , ecology , algorithm , electrical engineering , biology
Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very good startup to establish a rule of thumb in the laboratories to compare between observations. The importance of linear regression equations in predicting surface water quality parameters is a method that can be applied to any other location.