EGU2020-6690, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-6690
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Daily and hourly prediction of DO concentration using machine learning algorithm

heesung lim and hyunuk an
heesung lim and hyunuk an
  • Chungnam national university, University, Korea, Republic of (hyunuk@cnu.ac.kr)

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.

How to cite: lim, H. and an, H.: Daily and hourly prediction of DO concentration using machine learning algorithm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6690, https://doi.org/10.5194/egusphere-egu2020-6690, 2020

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