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RSS FeedsRemote Sensing, Vol. 15, Pages 1656: Prediction of Sea Surface Temperature in the South China Sea Based on Deep Learning (Remote Sensing)

 
 

18 march 2023 15:05:58

 
Remote Sensing, Vol. 15, Pages 1656: Prediction of Sea Surface Temperature in the South China Sea Based on Deep Learning (Remote Sensing)
 


Sea surface temperature is an important physical parameter in marine research. Accurate prediction of sea surface temperature is important for coping with climate change, marine ecological protection, and marine economic development. In this study, the SST prediction performance of ConvLSTM and ST-ConvLSTM with different input lengths, prediction lengths, and hidden sizes is investigated. The experimental results show that: (1) The input length has an impact on the prediction results of SST, but it does not mean that the longer the input length, the better the prediction performance. ConvLSTM and ST-ConvLSTM have the best prediction performance when the input length is set to 1, and the prediction performance gradually decreases as the input length increases. (2) Prediction length affects SST prediction. As the output length increases, the prediction performance gradually decreases. When other parameters are kept constant and only the output length is changed, the ConvLSTM gets the best result when the output length is set to 2, and the ST-ConvLSTM gets the best result when the output length is set to 1. (3) The setting of the hidden size has a great influence on the prediction ability of the sea surface temperature, but the hidden size cannot be set blindly. For ST-ConvLSTM, although the prediction performance of SST is better when the hidden size is set to 128 than when it is set to 64, the consequent computational cost increases by about 50%, and the performance only improves by about 10%.


 
106 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 15, Pages 1655: Prior Semantic Information Guided Change Detection Method for Bi-temporal High-Resolution Remote Sensing Images (Remote Sensing)
Remote Sensing, Vol. 15, Pages 1657: A Single Array Approach for Infrasound Signal Discrimination from Quarry Blasts via Machine Learning (Remote Sensing)
 
 
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