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Article

Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

1
Institute for Rural Engineering, National Agriculture & Food Research Organization (NARO), 2-1-6 Kannondai, Tukuba City, Ibaraki 305-8609, Japan
2
Center for Water Cycle, Marine Environment, and Disaster Management, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan
3
ARK Information Systems, INC, 4-2 Gobancho, Chiyoda-ku, Tokyo 102-0076, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Donghwi Jung
Water 2021, 13(8), 1109; https://doi.org/10.3390/w13081109
Received: 5 March 2021 / Revised: 13 April 2021 / Accepted: 14 April 2021 / Published: 17 April 2021
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions. View Full-Text
Keywords: reservoir water temperature; climate change; deep neural network; transfer learning approach reservoir water temperature; climate change; deep neural network; transfer learning approach
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MDPI and ACS Style

Kimura, N.; Ishida, K.; Baba, D. Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach. Water 2021, 13, 1109. https://doi.org/10.3390/w13081109

AMA Style

Kimura N, Ishida K, Baba D. Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach. Water. 2021; 13(8):1109. https://doi.org/10.3390/w13081109

Chicago/Turabian Style

Kimura, Nobuaki; Ishida, Kei; Baba, Daichi. 2021. "Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach" Water 13, no. 8: 1109. https://doi.org/10.3390/w13081109

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