Evaluation of river water quality by principal component analysis based water quality index and classify method using support vector machine
Monitoring water quality in the twenty-first century has become a major global concern. The Water Quality Index (WQI) is a useful method for determining the quality of drinking water in urban, rural, and industrial settings. Parameter selection, quality function determination for each parameter, and...
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Format: | Tagungsbericht |
Sprache: | eng |
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Zusammenfassung: | Monitoring water quality in the twenty-first century has become a major global concern. The Water Quality Index (WQI) is a useful method for determining the quality of drinking water in urban, rural, and industrial settings. Parameter selection, quality function determination for each parameter, and aggregation using mathematical equations are all part of a traditional WQI technique. A mathematical equation incorporates a number of water quality parameters to grade water quality and determine its acceptability for consumption. The dataset was treated to Principal Component Analysis (PCA) in order to extract the most essential WQI characteristics. The support vector machine technique is then used to locate outliers and categorize the water quality index. The proposed system was tested using the Southern Bug (or PivdennyiBooh) River dataset. The principal component analysis approach yielded a prediction accuracy of 95 percent, whereas the Support Vector Machine method generated a classification accuracy of 98 percent. |
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ISSN: | 0094-243X 1551-7616 |
DOI: | 10.1063/5.0173398 |