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- W3204992742 abstract "Internal solitary waves (ISWs) are observed in deep and shallow oceans with different propagation speeds ranging from over 3.0 m/s to less than 1.0 m/s. Different theories have been developed for ISWs propagating in different water depths. In this paper, a machine-learning-based propagation speed model for ISWs in shallow and deep oceans is developed based on synchronous satellite images. Optical satellite images in the South China Sea, Sulu Sea, and the Celebes Sea are collected with clear ISW signatures. Four machine learning techniques, namely fully connected network, supporting vector regression, K -nearest neighbor regression, and random forest, are used and compared to develop the model. The random forest model has the best performance with the highest coefficient of determination up to 0.97 and the smallest root mean square error of 0.07 m/s and 0.06 m/s on the training and test dataset. The influence of model input parameters is also discussed." @default.
- W3204992742 created "2021-10-25" @default.
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- W3204992742 date "2021-07-11" @default.
- W3204992742 modified "2023-10-17" @default.
- W3204992742 title "Satellite-Data-Driven Propagation Speed Model for Internal Solitary Waves in the Shallow and Deep Oceans" @default.
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- W3204992742 doi "https://doi.org/10.1109/igarss47720.2021.9554300" @default.
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