A machine learning approach to wastewater treatment: Gaussian process regression and Monte Carlo analysis
Nimra Nadeem, Zubair Khaliq, Abdulaziz Bentalib, Muhammad Bilal Qadir, Fayyaz Ahmad, Muhammad Wakil Shahzad, Abdulrahman Bin Jumah
Abstract
= 0.0277). The superiority of the GPR model was validated by comparing the Gaussian Process Regression Mean (RPAE value) of 0.92689 with the Polynomial Regression Mean (RPAE value of 2.2947). Besides, the simultaneous interpretation of the effects of the three predictors on the response variable was enabled using the GPR model, which is impossible when interpreting the polynomial regression model. Therefore, the GPR offers superior modeling, deeper insights, and reliable predictions, proving it to be a more sustainable and effective method for pollutant degradation in wastewater treatment than polynomial modeling.