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

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 ri...

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Veröffentlicht in:Water (Basel) Jg. 12; H. 12; S. 3399
Hauptverfasser: Baek, Sang-Soo, Pyo, Jongcheol, Chun, Jong Ahn
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2020
Schlagworte:
Chemical oxygen demand
Dams
Deep learning
Estuaries
Hydraulics
information systems
Korean Peninsula
Machine learning
Management
Neural networks
phosphorus
prediction
radar
rain
Rivers
total nitrogen
total organic carbon
Water
Water quality
watersheds
South Korea
Korean Peninsula
ISSN:2073-4441, 2073-4441
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Zusammenfassung: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.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w12123399