Machine learning methods for wastewater hydraulics

摘要: 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 affected significant limitations. In such cases, many issues can be means of machine learning algorithms, resulting from theories pattern recognition and computational learning. order to show potential an approach, this study Regression Tree M5P model, Bagging algorithm Random Forest were applied solution complex wastewater engineering: prediction energy loss, pool depth, air entrainment drop manhole, forecasting lateral outflow low crested side weir. The algorithms trained tested data obtained experimental tests carried out at Water Engineering Laboratory University Cassino Southern Lazio. most considered regression trees ensemble methods able provide very accurate predictions.