Efficient functioning of a sewer system: application of novel hybrid machine learning methods for the prediction of particle Froude number

Abstract

Sewer systems are usually built with a self-cleaning system that keeps the bottom of the channel free of sediment to lessen the effects of the constant buildup of sediment particles. Because of this, it is important to accurately predict the particle Froude number (Fr) when making sewer systems. For the prediction of Fr, five different sets of input variables were looked at. For the training and testing of the machine learning (ML) model, we used 10-fold cross-validation methodologies to prevent overfitting. M5Prime (M5P) model as a standalone and Bagging-M5P as a hybrid model were utilized, and the results were compared with the empirical equations proposed in the literature. Models perform best when all input variables are used for training and testing of models. The hybrid BA-M5P model performed better than the M5P model and empirical equations. We performed sensitivity analysis and compared the result based on MAE and MSE value, and we found sediment concentration (Svc) is the most important variable to predict the particle Froude number under non-deposition with deposited bed by best performing model BA-M5P. Hence, for the self-cleaning system, we prefer the BA-M5P ML model 26 with Svc the most required variable.