Deep Learning Methods for Predicting Tap-Water Quality Time Series in South Korea

Abstract

South Korea currently lacks a real-time monitoring and anomaly detection system for detecting continuous tap water quality changes from the water source to faucet and pre-diagnosing hazards that threaten tap water safety. In this study, we constructed an accurate water quality prediction model that could comprehensively cover all water treatment facilities supplying tap water nationwide and verified the model using an integrated approach. To address the uncertainty of continuously changing water quality, we collected five years (2017–2021) of hourly water quality data from 33 large water purification plants and applied various deep learning techniques to construct an optimal prediction model. We repeated water quality prediction and evaluation over the following 24 h through a time series cross-validation of an untrained dataset of the previous five months. The optimized deep learning model achieved average and maximum prediction accuracy of 98.78 and 99.98%, respectively, and showed excellent performance in terms of the root mean squared error (0.0006), mean absolute error (0.0003), and Nash–Sutcliffe efficiency (0.9894). Thus, deep learning technology greatly improved the accuracy and efficiency of water quality prediction. The proposed model could provide prompt and accurate water quality information for large-scale water supply facilities nationwide and improve public health through the early diagnosis of water quality anomalies.