Water quality prediction and classification based on principal component regression and gradient boosting classifier approach

Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression technique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic in...

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Veröffentlicht in:Journal of King Saud University. Computer and information sciences Jg. 34; H. 8; S. 4773 - 4781
Hauptverfasser: Md. Saikat Islam Khan, Nazrul Islam, Jia Uddin, Sifatul Islam, Mostofa Kamal Nasir
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Springer 01.09.2022
Schlagworte:
Boxplot analysis
Classification algorithm
Principal component regression
Water quality index
ISSN:1319-1578
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Zusammenfassung:Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression technique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic index method. Secondly, the principal component analysis (PCA) is applied to the dataset, and the most dominant WQI parameters have been extracted. Thirdly, to predict the WQI, different regression algorithms are used to the PCA output. Finally, the Gradient Boosting Classifier is utilized to classify the water quality status. The proposed system is experimentally evaluated on a Gulshan Lake-related dataset. The results demonstrate 95% prediction accuracy for the principal component regression method and 100% classification accuracy for the Gradient Boosting Classifier method, which show credible performance compared with the state-of-art models.
ISSN:1319-1578
DOI:10.1016/j.jksuci.2021.06.003