The Quality of Drinkable Water using Machine Learning Techniques

Abstract

Predicting potable water quality is more effective for water management and water pollution prevention. Polluted water causes serious waterborne illnesses and poses a threat to human health. Predicting the quality of drinkable water may reduce the incidence of water-related diseases. The latest machine learning approach has shown promising predictive accuracy for water quality. This research uses five different learning algorithms to determine drinking water quality. First, data is gathered from public sources and presented in accordance with World Health Organization (WHO) water quality standards. Several parameters, including hardness, conductivity, pH, organic carbon, solids, and others, are essential for predicting water quality. Second, Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Deep Neural Network (DNN), and Gaussian Nave Bayes are used to estimate the quality of the drinking water. The conventional laboratory technique for assessing water quality is time-consuming and sometimes costly. The algorithms proposed in this work can predict drinking water quality within a short period of time. ANN has 99 percent height accuracy with a training error of 0.75 percent during the training period. RF has an F1 score of 87.86% and a prediction accuracy of 82.45%. An Artificial Neural Network (ANN) predicted height with an F1 score of 96.51 percent in this study. Using an extended data set could improve how well predictions are made and help stop waterborne diseases in the long run.