Development of Predictive Models for “Very Poor” Beach Water Quality Gradings Using Class-Imbalance Learning

Jiuhao Guo, Joseph Hun‐wei Lee

2021Environmental Science & Technology16 citationsDOI

Abstract

concentration exceeds 610 counts/100 mL. Models are developed for three marine beaches with different hydrographic and pollution characteristics using a 30 year data set spanning three periods with different water quality status. The model-data comparison over a wide range of conditions shows that the proposed method results in a significant improvement in the prediction of "very poor" water quality. The proposed class-imbalance method for predicting rare events has an F-score of 0.84, and it significantly outperforms MLR and classification tree (CT) models with corresponding F-scores of 0.39 and 0.69. A robust beach water quality forecast system can hence be developed using hybrid MLR-binary classification modeling.

Topics & Concepts

Water qualityPredictive modellingDecision treeMean squared prediction errorEnvironmental scienceIndicator bacteriaLinear regressionHydrology (agriculture)StatisticsComputer scienceMachine learningMathematicsFecal coliformEcologyGeologyGeotechnical engineeringBiologyImbalanced Data Classification TechniquesHydrological Forecasting Using AIMachine Learning and Data Classification