Prediction of the DO Factor at Bugok Bridge, Oncheoncheon, Using Deep Learning

Abstract

The use of monitoring in river management is known to be both economical and rational, and the amount of digital information globally is increasing over time. AI research utilizing such data has been widely employed recently in the field of water resources and hydrology, yielding excellent predictive results. In this study, we utilized DO (Dissolved Oxygen) factor and meteorological data collected from the Bugok Bridge site in the Oncheoncheon watershed through an automatic water quality measurement network. We employed the LSTM (Long Short Term Memory) algorithm, a type of deep learning known for its excellent time series learning capabilities, as the learning algorithm. To confirm the potential of the use of big data, we conducted a comparative analysis by performing hourly and daily predictions, and an accuracy analysis by comparing actual and predicted data. For data utilization, missing data from the data collected by the automatic measurement network were linearly interpolated. It was confirmed that the predictive performance for the DO factor was higher using hourly than daily data.