Quality Check of Water for Human Consumption Using Machine Learning

Abstract:

The greatest threat to humanity is water pollution. It causes affliction to animals, plants, etc. To avoid the problem in the transportation sector, we need to foretell water standards from pollution using machine learning methods. monitoring and forecasting the value of water has become a vital area to research. The goal is to examine machine learning methods for water quality forecasting by predicting the results to the best accuracy. Dataset is analysed by Supervised Machine Learning Technique (SMLT) to bag a number of details such as, variable identification: uni-variate analysis, bi-variate analysis and multivariate analysis, lost quantity treatment and data validation analysis, data purification / preparation and data detection will be performed on the dataset. The analysis provides a clean guide to examine the sharpness of the model parameters in relation to fulfilment in predicting water standard by calculating its efficiency. To offer a method to accurately predict the Water Quality Index (WQI) value by predicting the results in the form of accuracy from comparing supervised classification machine learning algorithms. Furthermore, to correlate and canvas the effectiveness of various algorithms from the given dataset with evaluation of classification report, confusion matrix, categorizing data from priority and the result shows that the performance of the suggested algorithms that can be compared with Accuracy which is done by evaluating Precision, Recall and F1 Score of the algorithm.

• Periodical: Advances in Science and Technology (Volume 124)
• Pages: 574-589
• DOI: https://doi.org/10.4028/p-17jlv6
• Online since: February 2023
• Authors: J.M. Nitharshni*, R. Nilasruthy, K.R. Shakthi Akshaiya, M. Rajavel
• Keywords: Accuracy, Classification Model, Data Science, Machine Learning, Machine Learning Algorithms, Supervised Machine Learning Techniques, Water Quality

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