Construction of Sea Surface Temperature Forecasting Model for Bohai Sea and Yellow Sea Coastal Stations Based on Long Short-Time Memory Neural Network

Autor: Yan Jiao, Ge Li, Peng Zhao, Xue Chen, Yongzheng Cao, Guiyan Liu, Lingjuan Wu, Xin Xu, Di Fu, Ruoxue Xin, Chengzhen Ji
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Water, Vol 16, Iss 16, p 2307 (2024)
Druh dokumentu: article
ISSN: 16162307
2073-4441
DOI: 10.3390/w16162307
Popis: In order to address the issue of large errors in predicting SST along the coast using numerical models, this study adopts LSTM, a deep learning method, to develop optimal SST prediction models. The Xiaomaidao Station is selected as an example, and then the method is then extended to 14 coastal stations along the Bohai Sea and the Yellow Sea. The results show that the SST prediction model based on LSTM effectively improves forecast accuracy. The mean absolute errors for 1–3-day SST forecasts of the optimal model at Xiaomaidao Station are 0.20 °C, 0.27 °C, and 0.31 °C, and the root mean square errors are 0.28 °C, 0.36 °C, and 0.41 °C, respectively, representing an average reduction of 78% compared to those of the numerical model. Extending this approach to other forecasting sites along the Bohai Sea and the Yellow Sea results in an average 61% reduction in forecast error when compared with the numerical model. Furthermore, it is found that utilizing an LSTM model can significantly save computational resources and improve the forecasting efficiency.
Databáze: Directory of Open Access Journals