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Water Quality Prediction Using PCA and GBC by EsmeNio PaUlo is a document available to read on EtoBox.
This paper presents a water quality prediction model using principal component regression and a gradient boosting classifier, achieving 95% prediction accuracy and 100% classification accuracy on a dataset from Gulshan Lake. The model calculates the water quality index (WQI) through a weighted arithmetic index method and applies principal component analysis to extract dominant parameters. The study highlights the advantages of machine learning techniques over traditional methods in monitoring water quality
- Author
- EsmeNio PaUlo
- Language
- EN