Daily and hourly prediction of DO concentration using machine learning algorithm

Abstract

<p>In order to perform adequate water quality management, it is important to predict the water quality through measurement and data accumulation of the concentration of contaminants. However, daily measurement of water quality pollutant is unrealistic in practical aspect. In this study, the possibility of daily- or hourly-based water quality prediction through dissolved oxygen (DO) using RNN-LSTM (Recurrent Neural Network-Long Short-term Memory) algorithm, which is well-known for time-series learning, was performed. The research selected Bugok Bridge in Oncheon-stream, Busan, South Korea. Hourly-based DO, temperature, wind speed, relative humidity, rainfall data was collected at the target location and was converted to daily data. To forecast the DO concentration, TensorFlow, a deep learning open source library developed by Google, was utilized. Data of four years (2014-2017) was used for daily learning data and 2018 data was used for verification of the trained model. The performance with the adjusted number of hidden layers, number of repetitions, and the sequence length, as well as the accuracy of the model was analyzed. As a result of this research, it is proven that the performance of the prediction can be improved when weather data and large amount of data is available.</p>

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

“…RNN은 이전의 정보를 현재의 문제 해결에 활용하여 순차 적으로 데이터를 처리하는 구조로 현재의 문제를 해결하는 중요한 정보를 가진 데이터가 오랜 시간 전에 발생하였을 때 오래전 기억이 소실되어 이를 반영하기 장기 의존성 문제 가 있다 (Lim et al, 2020). 장단기 메모리(Long Short-Term Memory)는 순환신경망의 장기 의존성 문제를 해결하기 위해 Hochreiter and Schmidhuber (1997 (Olah, 2015).…”