What factors are important in classification prediction WATER quality by machine learning?
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Machine learning algorithms can be used to predict water quality based on various factors. Parameters such as alkalinity, conductivity, pH value, turbidity, hardness, dissolved solids, nitrate, sulphate, total hardness, dissolved oxygen, biological oxygen demand (BOD), and total coliform are important in classifying and predicting water quality . These factors are used as input to machine learning models such as decision tree classifiers, support vector machines (SVM), random forest, K-nearest neighbor (K-NN), naive Bayes, and decision trees . The accuracy of the prediction models can be evaluated using metrics such as precision, recall, F1-score, and confusion matrix . By analyzing and predicting water quality using machine learning, it is possible to ensure access to clean drinking water and protect public health and the environment .
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26 Jun 2023 | The important factors for classification prediction of water quality using machine learning are turbidity, pH, specific conductivity, dissolved solids, nitrate, sulphate, total hardness, dissolved oxygen, B.O.D, and total coliform. |
| The important factors for classification prediction of water quality using machine learning include pH value, turbidity, hardness, conductivity, and dissolved solids in water. | |
| The alkalinity and conductivity of water are important factors in predicting water quality using machine learning. | |
| The alkalinity and conductivity of water are important factors in predicting water quality using machine learning. |
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