Environment and Ecology Research Vol. 11(4), pp. 537 - 542
DOI: 10.13189/eer.2023.110402
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Predicting the Water Potability Index Using Machine Learning


Ivan Ivanov , Borislava Toleva *
Faculty of Economics and Business Administration, Sofia University, Bulgaria

ABSTRACT

Water potability is a key topic in ecology as it defines the areas where life can exist and the quality of health and food. Without potable water, vast regions can be unpopulated. Poor water quality affects the quality and quantity of food and the spread of diseases. There is a tendency that sources of potable water have started to deteriorate in recent years, so the topic of potable water quality has become central in environmental and ecology studies. A central question is prediction of water quality in various areas. This research proposes an improved machine learning algorithm for predicting the potability of water. The proposed algorithm is simple, and it is easier to apply compared to other existing algorithms. It can be applied to various datasets for water quality providing a quick insight into the question whether new water sources in the area are more likely to have potable water. It can also be applied to various datasets about water quality. Therefore, a quick review of the water and its quality in each region can be done using the proposed algorithm.

KEYWORDS
Water Potability, Machine Learning, Classification, Unbalanced Data

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Ivan Ivanov , Borislava Toleva , "Predicting the Water Potability Index Using Machine Learning," Environment and Ecology Research, Vol. 11, No. 4, pp. 537 - 542, 2023. DOI: 10.13189/eer.2023.110402.

(b). APA Format:
Ivan Ivanov , Borislava Toleva (2023). Predicting the Water Potability Index Using Machine Learning. Environment and Ecology Research, 11(4), 537 - 542. DOI: 10.13189/eer.2023.110402.