Machine Learning-based Water Potability Prediction

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dc.contributor.authorAlnaqeb, Reem
dc.contributor.authorAlrashdi, Fatema
dc.contributor.authorAlketbi, Khuloud
dc.contributor.authorIsmail, Heba
dc.date.accessioned2024-06-19T12:51:27Z
dc.date.available2024-06-19T12:51:27Z
dc.date.issued2022
dc.description.abstractProtecting and caring for water is one of the most critical environmental problems today. This research aims to design an intelligent system using machine learning models to improve water quality and predict whether it is safe to be used as drinking water. Several models of machine learning algorithms are compared to find the best model to be used for the accuracy of prediction of water quality. In this research, we compare Decision Tree, K-Nearest Neighbor, Support Vector Machine, Ransom Forest, and LightGBM models to get the best model for water potability prediction. Experimental results show that LightGBM model produced the best prediction accuracy of 99.74% on the experimental data. © 2022 IEEE. keywords: Decision Tree, K-Nearest Neighbor, LighGBM, Machine learningen
dc.identifier.citationAlnaqeb, R., Alrashdi, F., Alketbi, K., & Ismail, H. (2022, December). Machine learning-based water potability prediction. In 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-6). IEEE.
dc.identifier.doihttps://doi.org/10.1109/AICCSA56895.2022.10017579
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/5830
dc.language.isoen
dc.publisherIEEE Xplore
dc.titleMachine Learning-based Water Potability Prediction
dc.typeConference Paper

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