Prediction of sea surface temperature in the Gulf of Thailand using neural network approach

2003 ◽  
Author(s):  
Ramesh Kumar Gautam ◽  
So Kazama ◽  
Suranjan Panigrahi
Keyword(s):  
Neural Network ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Gulf Of Thailand ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Gulf Of Thailand

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1609-1622 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Variance ◽  
Temperature Data ◽  
Satellite Observations ◽  
Sea Surface ◽  
Function Decomposition ◽  
Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

scholarly journals Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network

2020 ◽  
Vol 8 (4) ◽  
pp. 249 ◽  
Author(s):  
Zhen Zhang ◽  
Xinliang Pan ◽  
Tao Jiang ◽  
Baikai Sui ◽  
Chenxi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Short Term Memory ◽  
Absolute Error ◽  
Sea Surface ◽  
Neural Network Algorithm ◽  
Stability And Accuracy ◽  
Gated Recurrent Unit

The sea surface temperature (SST) is an important parameter of the energy balance on the Earth’s surface. SST prediction is crucial to marine production, marine protection, and climate prediction. However, the current SST prediction model still has low precision and poor stability. In this study, a medium and long-term SST prediction model is designed on the basis of the gated recurrent unit (GRU) neural network algorithm. This model captures the SST time regularity by using the GRU layer and outputs the predicted results through the fully connected layer. The Bohai Sea, which is characterized by a large annual temperature difference, is selected as the study area, and the SSTs on different time scales (monthly and quarterly) are used to verify the practicability and stability of the model. The results show that the designed SST prediction model can efficiently fit the results of the real sea surface temperature, and the correlation coefficient is above 0.98. Regardless of whether monthly or quarterly data are used, the proposed network model performs better than long short-term memory in terms of stability and accuracy when the length of the prediction increases. The root mean square error and mean absolute error of the predicted SST are mostly within 0–2.5 °C.

Prediction of sea surface temperature using a multiscale deep combination neural network

2020 ◽  
Vol 11 (7) ◽  
pp. 611-619
Author(s):  
Lingyu Xu ◽  
Yifan Li ◽  
Jie Yu ◽  
Qin Li ◽  
Suixiang Shi
Keyword(s):  
Neural Network ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface
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