Leveraging Supervised Machine Learning Algorithms in Predicting Contaminants Removal From Novel Cascade Aerating Trickling Filter
This research evaluated machine learning (ML) algorithms to predict the efficiency of a cascade aerating trickling filter (CATF) in removing physicochemical characteristics such as suspended solids (SS), biological oxygen demand (BOD) and chemical oxygen demand (COD). The pre‐evaluation of characteristics' data demon...