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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality
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Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

机译：Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

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摘要

Over the last few years, different contaminants have posed a danger to the quality of the water. Hence modelling and forecasting water quality are very important in the management of water contamination. The paper proposes an ensemble machine learning-based model for assessing water quality. The results of the proposed model are compared with several machine learning models, including k-nearest neighbour, Na?ve Bayes, support vector machine, and decision tree. The considered dataset contains seven statistically important parameters: pH, conductivity, dissolved oxygen, Biochemical Oxygen Demand, nitrate, total coliform, and fecal coliform. The water quality index is calculated for assessing water quality. To utilize an ensemble approach, a voting classifier has been designed with hard voting. The highest prediction accuracy of 99.5% of the water quality index is presented by the voting classifier as compared to the prediction accuracy of 99.2%, 90%, 79%, and 99% presented through k-nearest neighbour, Na?ve Bayes, support vector machine, and decision tree, respectively. This was further enhanced to 99.74% using particle swarm based optimization.

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