Optimizing Water Quality Parameters Using Machine Learning Algorithms

Abstract

Water is the necessity of life; without water, human being not survives, but people are polluting the water. Water pollution is the major problem today and affects the groundwater quality. The main causes of water pollution are industries’ waste product disposal, urbanization, crowded population, wastewater, sewage waste, and harmful chemicals’ released by industries. There is an urgent need to resolve this issue in order for us to have safe drinking water. This article proposes a suitable classification model for classifying water quality that is based on machine learning algorithms and can be used to classify water quality. An evaluation and comparison of the performance of various classification models, visualizations, comparisons, and algorithms were carried out in order to identify the significant features that contributed to classifying the water quality of groundwater in Ambala, Haryana. Three models, each with its own set of algorithms, were tested, and their results were compared to each other as well. According to the results, the random forest algorithm was the best classification model out of the five models tested, with the highest accuracy of 82.67% compared to the other models. Overall, wastewater is hazardous to our health, and using scientific models to solve this problem is an absolute must.