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Article

Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
Climate Analytics Department, APEC Climate Center, Busan 48058, Korea
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3399; https://doi.org/10.3390/w12123399
Received: 9 October 2020 / Revised: 3 November 2020 / Accepted: 30 November 2020 / Published: 3 December 2020
(This article belongs to the Special Issue Water-Quality Modeling)
A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality. View Full-Text
Keywords: water quality prediction; deep learning; convolutional neural network (CNN); long short-term memory (LSTM) network water quality prediction; deep learning; convolutional neural network (CNN); long short-term memory (LSTM) network
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MDPI and ACS Style

Baek, S.-S.; Pyo, J.; Chun, J.A. Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach. Water 2020, 12, 3399. https://doi.org/10.3390/w12123399

AMA Style

Baek S-S, Pyo J, Chun JA. Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach. Water. 2020; 12(12):3399. https://doi.org/10.3390/w12123399

Chicago/Turabian Style

Baek, Sang-Soo, Jongcheol Pyo, and Jong A. Chun 2020. "Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach" Water 12, no. 12: 3399. https://doi.org/10.3390/w12123399

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