Ridge Logistic Regression for Binary Water Quality Classification under Severe Multicollinearity: A Monte Carlo and Real River Study

Jafaraghaie, Razieh

doi:10.22054/jdsm.2026.94308.1101

Ridge Logistic Regression for Binary Water Quality Classification under Severe Multicollinearity: A Monte Carlo and Real River Study

Document Type : original

Author

Razieh Jafaraghaie

Department of Mathematics and Statistics, Sho. C., Islamic Azad University, Shoushtar, Iran

https://doi.org/10.22054/jdsm.2026.94308.1101

Abstract

Classification of water quality into acceptable and unacceptable categories is essential for public health and environmental protection. However, standard maximum likelihood logistic regression (MLE) often becomes unstable in real-world hydrochemical datasets due to severe multicollinearity. This study evaluates the performance of ridge-penalized logistic regression compared to standard MLE through a Monte Carlo simulation and a real-world application using river water data from Buenos Aires, Argentina.

While MLE demonstrated marginally better in-sample predictive metrics in controlled simulations with moderate collinearity, it suffered from severe non-convergence and coefficient divergence in the real dataset, where predictors exhibited near-perfect correlation. In contrast, ridge regularization provided highly stable, interpretable coefficient estimates and robust performance by effectively managing the bias-variance tradeoff.
These findings emphasize that ridge regularization is not merely a theoretical improvement, but a practical necessity for binary water quality classification when hydrochemical predictors are highly correlated. I recommend routine use of cross-validated ridge penalization in environmental modeling, while explicitly acknowledging the limitations of in-sample performance metrics in small, imbalanced datasets.

Keywords

Binary water quality classification

Monte Carlo simulation

Variance inflation factor

River water parameters

Subjects