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.
Faculty of Advanced Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan.
Sci Total Environ. 2020 Jun 10;720:137613. doi: 10.1016/j.scitotenv.2020.137613. Epub 2020 Feb 27.
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.
本研究在每小时的时间尺度上开发了一种基于长短期记忆 (LSTM) 网络的沿海海平面估算模型，该模型是一种递归神经网络。该模型纳入了各种现象对沿海海平面的影响，例如太阳和月亮的引力、季节性、风暴潮和气候变化。模型模拟中利用了月亮和太阳相对于目标位置在每个小时的相对位置，以反映太阳和月亮的引力。模型还考虑了每个小时目标点附近的风速和风向、平均海平面气压 (MSLP) 和空气温度，以考虑风暴潮和沿海海平面季节性的影响。除了每小时的局部变量外，还考虑了年度全球平均气温作为模型的输入，以反映全球变暖对沿海海平面的影响。该模型使用年度全球平均气温的多个输入长度来实现，以估算日本大阪测站的沿海海平面。使用了几个统计指标，如平均值、纳什-苏特克里夫效率和均方根误差，来评估模型性能。结果表明，所提出的模型准确地重建了太阳和月亮引力对沿海海平面的影响。该模型还考虑了风速和 MSLP 波动的影响，尽管在强风和低 MSLP 条件下沿海海平面被低估。最后，引入更长持续时间的年度全球平均气温提高了模型的准确性。因此，最佳结果在测试过程中显示 NSE 值为 0.720。
