DOI: 10.1016/j.scitotenv.2020.137613
论文题名: Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network
作者: Ishida K. ; Tsujimoto G. ; Ercan A. ; Tu T. ; Kiyama M. ; Amagasaki M.
刊名: Science of the Total Environment
ISSN: 489697
出版年: 2020
卷: 720 语种: 英语
英文关键词: Coastal engineering
; Deep learning
; ERA5
; Global warming
; Recurrent neural network
; Sea level rise
Scopus关键词: Atmospheric temperature
; Brain
; Climate models
; Coastal engineering
; Deep learning
; Deep neural networks
; Error statistics
; Floods
; Global warming
; Mean square error
; Moon
; Recurrent neural networks
; Sea level
; Storms
; Testing
; Wind
; ERA5
; Gravitational attraction
; Mean air temperatures
; Mean sea level pressures
; Relative positions
; Root mean square errors
; Sea level rise
; Wind speed and directions
; Long short-term memory
; algorithm
; artificial neural network
; coastal morphology
; computer simulation
; efficiency measurement
; global warming
; machine learning
; model validation
; sea level change
; air temperature
; article
; climate
; deep learning
; greenhouse effect
; Japan
; long short term memory network
; sea level rise
; seasonal variation
; simulation
; storm surge
; sun
; wind speed
英文摘要: In this study, a coastal sea level estimation model was developed at an hourly temporal scale using the long short-term memory (LSTM) network, which is a type of recurrent neural networks. The model incorporates the effects of various phenomena on the coastal sea level such as the gravitational attractions of the sun and the moon, seasonality, storm surges, and changing climate. The relative positions of the moon and the sun from the target location at each hour were utilized to reflect the gravitational attractions of the sun and the moon in the model simulation. The wind speed and direction, mean sea level pressure (MSLP), and air temperature near the target point at each hour were used to consider the effects of storm surges and seasonality of the coastal sea level. In addition to the hourly local variables, the annual global mean air temperature was considered as input to the model to reflect the effect of global warming on the coastal sea level. The model was implemented using several input lengths of the annual global mean air temperature to estimate the coastal sea level at the Osaka gauging station in Japan. Several statistics such as the mean, the Nash–Sutcliffe efficiency, and the root mean square error were used to evaluate model performance. The results show that the proposed model accurately reconstructed the effects of the gravitational attractions of the sun and the moon on the coastal sea levels. The model also considered the effects of fluctuations in the wind speed and MSLP although the coastal sea levels during were underestimated strong winds and low MSLP conditions. Lastly, introducing a longer duration annual global mean air temperature improved model accuracy. Consequently, the best results show 0.720 of the NSE value for the test process. © 2020 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158096
Appears in Collections: 气候变化与战略
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作者单位: International Research Organization for Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto, 860-8555, Japan; Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto, 860-8555, Japan; Department of Civil and Environmental Engineering, University of California, Davis, One Shields Avenue, Davis, CA 95616, United States; Department of Environmental Science, Policy, & Management, University of California, Berkeley, United States
Recommended Citation:
Ishida K.,Tsujimoto G.,Ercan A.,et al. Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network[J]. Science of the Total Environment,2020-01-01,720