ML is useful in detecting drinking water nitrification (Table ). NB classifier, a supervised ML model, relies on biomass and microbiome datasets to detect nitrification in engineered water systems (Gomez-Alvarez and Revetta, 2020). After being trained with microbial indicators, the model has a binary classification accuracy of up to 85% with an AUC of 0.825 when distinguishing between nitrification and stable events. Nitrification can also be monitored using spectrum fingerprint since one can isolate the combined nitrate and nitrite from the total spectra. SVR was used and trained to predict the concentrations of nitrate and nitrite from nitrate/nitrite spectra at various wavelengths (Figure ) . SVR negates the need for any chemical supplements, is easy to use, and can reach a high level of precision of up to ±0.01 mg N/L.