Author(s): Hossein Amini; Man Yue Lam; Reza Ahmadian
Linked Author(s): Man Yue Lam, Reza Ahmadian
Keywords: No Keywords
Abstract: Water quality is a critical research area with significant social and environmental implications, necessitating collaborative efforts among researchers, policymakers, and the public. This study explores dimensionality reduction techniques and machine learning algorithms for predicting faecal indicator organisms (FIOs) such as E. coli and Enterococci. Principal component analysis (PCA) served as the dimensionality reduction model, and the prediction accuracy of the dimension-reduced variables was tested with a linear regression model. The analysis reveals that four principal components capture approximately 80% of the data variability.
Year: 2024