Machine learning methods for wastewater hydraulics

Abstract Wastewater hydraulics problems are frequently addressed by investigation on physical models. Dimensional analysis is a powerful tool that allows discovering essential information about the investigated phenomenon, but in some cases it is affected by significant limitations. In such cases, many issues can be addressed by means of machine learning algorithms, resulting from the theories on pattern recognition and computational learning. In order to show the potential of such an approach, in this study Regression Tree M5P model, Bagging algorithm and Random Forest algorithm were applied to the solution of some complex problems of wastewater engineering: the prediction of energy loss, the pool depth, the air entrainment in a drop manhole, and the forecasting of the lateral outflow in a low crested side weir. The algorithms were trained and tested on data obtained from experimental tests that were carried out at the Water Engineering Laboratory of the University of Cassino and Southern Lazio. In most of the considered cases, regression trees and ensemble methods were able to provide very accurate predictions.