Three studies developed multiple ML approaches to detect anomalies in the drinking water quality datasets from GECCO Industrial Challenges (GECCO IC) . These studies pinpointed shifts or spotted anomalies in drinking water quality over time. Various parameters such as pH, redox potential, electric conductivity, turbidity, and chlorine dioxide concentration are the predictors, whereas events in Boolean form are the outputs. A study developed SVM, DNN, long short-term memory (LSTM), recurrent NN (RNN), LR, simple NN, and linear discriminant analysis (LDA) to detect water quality anomaly in the dataset from 2017 GECCO IC . SVM shows the highest performance with an F1-score of 0.99 in cross-validation. Nevertheless, all the models have poor performance with the unseen test dataset with a maximum F1-score of 0.36. In the other two studies focusing on the dataset from GECCO IC 2018, LSTM demonstrates superior results, scoring a higher F1-score than traditional models such as LR and SVM with F1-scores of 0.80 and 0.78, respectively .