Prediction of the DO concentration using the machine learning algorithm: case study in Oncheoncheon, Republic of Korea

Heesung Lim1   Hyunuk An1,*   Eunhyuk Choi2   Yeonsu Kim3,*   

1Agricultural and Rural Engineering, Chungnam National University, Daejeon 34134, Korea
2Rural research institute, Korea Rural Community Corporation, Ansan 15634, Korea
3Korea Water Resources Corporation, Daejeon 34350, Korea


The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

Figures & Tables

Fig. 1. Structure of recurrent neural network long-short term memory (RNN-LSTM). C, cell state; f, forget gate; h, hidden state; i, input gate; o, output gate.