Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

Abstract

Drinking water quality data sets used in learning models have been highly imbalanced, which has weakened the prediction ability of models for drinking water quality. Although some efforts have been made to address the issue of imbalance, little is known about the suitable technologies for drinking water quality prediction. Here, a total of 16 common learning models were applied individually to compare the drinking water quality prediction performance based on a large-scale highly imbalanced drinking water quality data set. Our results showed that ensemble, cost-sensitive learning models with higher F1-scores were more suitable for predicting drinking water quality, compared to other models tested in this study. In addition, the learning model performance could be enhanced by the introduction of two mainstream sampling algorithms [synthetic minority oversampling technique (SMOTE) combined with the Tomek links technique (TLTE) or the edited nearest neighbor technique (ENNTE), SMOTE + TLTE or SMOTE + ENNTE, respectively]. In particular, the F1-scores of deep cascade forest (DCF) with SMOTE + TLTE or SMOTE + ENNTE reached 94.54 ± 2.51% and 94.68 ± 2.72%, respectively. As a consequence, DCF with these two sampling algorithms has greater potential to be applied in drinking water quality monitoring and prediction, as well as other fields that have suffered from issues of imbalanced data.

Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

“…The improvement over the baseline performance was obtained by changing only a single hyperparameter of the base learners of the ANN-EFS: the cost function. This is consistent with findings from several other fields including inventory management [41], flood modelling [42,49], fraud detection [50], epidemiology [52], and drinking water quality modelling [54,55], all of which have shown that changing the error metric and implementing cost-sensitive learning is much more effective than using standard symmetrical error metrics and cost insensitive learning. However, the present study shows this for the first time when using probabilistic EFS.…”

“…In drinking water research, two recent studies have proposed ANN model building frameworks [80,81], however, neither of these studies include guidance on the selection of an appropriate cost function. Based on the substantial improvements in performance obtained in this study when training the ANN-EFS with the selected cost function as opposed to a default, as well as the improvements over cost-insensitive training obtained in other drinking water studies [54,55], we recommend that future model development frameworks for drinking water modelling should also include consideration for selection of an appropriate cost function.…”

“…The second weighting, class-based weighting by FRC, also prioritizes observations with low household FRC, however, in this case, observations were first grouped into classes based on their household FRC value and then a weight was assigned to each class, instead of to each observation. Class-based weighting is a common cost-sensitive learning approach for classification models when prioritizing specific classes [45,54,55,62,65]. The thresholds used to group the observations into classes were selected based on groupings used in literature and water quality guidelines for humanitarian response [66][67][68]:…”

“…Alternative error metrics and cost-sensitive learning have been applied for regression and classification modelling to predict rare or high-priority events [47] such as flooding [45,48,49]; fraudulent credit card purchases [50]; fault detection in machinery [51]; cholera cases [52] and for differentiating between benign and malignant cysts for cancer detection [53]. They have even been applied for anomaly detection and compliance monitoring in water treatment [54,55]. However, these methods have not been applied to probabilistic models like EFS.…”

AI and Big Data in Water Environments

“…It is only after such an effort that we can build more meaningful models to address the research questions mentioned above. Second, when modeling drinking water quality in China based on six important indicators (pH, electric conductivity, turbidity, spectral absorption coefficient, water temperature, and pulse−frequency−modulation value of the sensor panel), Chen et al 8 discovered that, although there is a large amount of data on drinking water quality, the data sets are highly imbalanced; that is, the majority of the treated drinking water meets the requirements and there is a rare occurrence (1.79‰) of unqualified drinking water events. To still develop robust ML models, the authors reduced the degree of data set imbalance by employing different combinations of mixed sampling algorithms: the synthetic minority oversampling technique (SMOTE), the Tomek links technique (TLTE), and the edited nearest neighbor technique (ENNTE).…”