Application of deep learning approaches to predict monthly stream flows

Accurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gated recurrent unit (GRU) neural network, recurrent n...

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Bibliographic Details
Published in:Environmental monitoring and assessment 2023-06, Vol.195 (6), p.705-705, Article 705
Main Authors: Dalkilic, H. Yildirim, Kumar, Deepak, Samui, Pijush, Dixon, Barnali, Yesilyurt, S. Nur, Katipoğlu, O. Mert
Format: Article
Language:English
Subjects:
Artificial Intelligence
Atmospheric Protection/Air Quality Control/Air Pollution
Correlation coefficient
Correlation coefficients
Deep Learning
Drought
Earth and Environmental Science
Ecology
Ecotoxicology
Environment
Environmental Management
Environmental monitoring
Environmental Monitoring - methods
Environmental risk
Environmental science
Estimation
Flood management
Hydroelectric power
Hydroelectric power generation
Long short-term memory
Machine learning
Monitoring/Environmental Analysis
Neural networks
Neural Networks, Computer
Recurrent neural networks
Risk management
River flow
Rivers
Root-mean-square errors
Stream discharge
Stream flow
Volumetric efficiency
Water resources
Water use
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Summary:Accurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gated recurrent unit (GRU) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) to predict with river flows at three different streamflow observation stations in Erzincan, Bayburt, and Gümüshane. Monthly streamflow time series covering the years 1978 to 2015 were used to set up artificial intelligence models. During the modeling phase, 70% of the data was divided into training (October 1978–April 2004), 15% validation (May 2004–September 2009), and 15% test set (October 2010–September 2015). Model performances were made according to the correlation coefficient, root mean square error, the ratio of RMSE to the standard deviation, Nash–Sutcliffe efficiency coefficient, index of agreement, and volumetric efficiency values. The calculation results show that GRU leads efficient estimation results for estimating streamflow and can also be used in allied water resources.
ISSN:0167-6369
1573-2959
DOI:10.1007/s10661-023-11331-5