An Antifouling Redox Sensor with a Flexible Carbon Fiber Electrode for Machine Learning-Based Dissolved Oxygen Prediction in Severely Eutrophic Waters

Abstract

Machine-learning-based models are used to predict dissolved oxygen (DO); however, acquiring continuous water quality data for input variables in harsh environments remains challenging. Herein, redox potential (ORP) determined by a thermo-treated flexible carbon fiber electrode was introduced as a single or preferential input variable for machine-learning-based DO prediction in a year-round eutrophic estuary. The novel ORP sensor was operated for 4 months, and DO was predicted from ORP and six water quality data sources using a long short-term memory (LSTM) neural network. ORP and DO concentration showed a linear correlation, but the first-order correlation slopes varied seasonally. The optimal LSTM hyperparameters were proposed, which depended on the prediction time step and predictor case. Simulation results showed higher seasonal DO dynamics reproduced using ORP alone (RMSE = 1.09) than that predicted using six other water quality parameters (RMSE = 1.32). In addition, ORP played a key role in DO prediction when combined with all water quality parameters (RMSE = 1.08). The feature importance of ORP as a predictor was evaluated from a random forest model. Overall, the highly selective redox sensor has a distinct response to DO concentration and offers a novel and cost-effective approach for monitoring or predicting DO in eutrophic waters.

Keywords

• oxidation–reduction potential
• electrochemical sensor
• coastal hypoxia
• input variable
• LSTM network
• random forest model