Application of Binary Classification Modelling Techniques for Water Potability Prediction
Abstract
Consuming safe water is essential for maintaining good health throughout life, regardless of an individual's susceptibility to pollutants. However, certain people, such as infants, small children, the elderly, and those residing in unsanitary conditions, are at a higher risk of contracting water-borne illnesses. To instil public confidence in water safety, a comprehensive monitoring and control system must be in place for the water supply. Precise and efficient prediction is difficult because of the numerous hydrological and environmental processes that affect water quality. In unmonitored watersheds, the difficulty is considerably greater. In this context, the binary classification models can potentially provide reliable solutions. The three (3) developed models are Logistic Regression, Decision Forest, and Support Vector Machine to improve the water quality predictions in unmonitored watersheds. Results showed that the SVM model performed the best in predicting water potability.
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- Application of Binary Classification Modelling Techniques for Water Potability Prediction
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Published In
May 2024
324 pages
ISBN:9798400716386
DOI:10.1145/3674558
Copyright © 2024 ACM.
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Association for Computing Machinery
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Published: 26 August 2024
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ICCTA 2024
ICCTA 2024: 2024 10th International Conference on Computer Technology Applications
May 15 - 17, 2024
Vienna, Austria
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