High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things

Abstract

Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and Temporal Convolutional Network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.

Deep Learning Analysis of Impact of Arctic Sea Extent Over Indian Ocean Sea Surface Temperature

“…As identified from survey, there were no existing works on prediction of SST from Arctic sea extent, [19] 18.35 Sun et al [29] 16.35 EEIO Proposed 13.89 Tripathi et al [19] 18.40 Sun et al [29] 16.41…”

DeepBlue: Advanced convolutional neural network applications for ocean remote sensing

“…When combined with various CNN models and CNN methods in the outer circles, it can propel advancements in different domains within ocean remote sensing (the outermost circle). For example, CNNs have been used for temperature [71], [72], salinity, wind field, significant wave height (SWH), and sea ice prediction [73], [74], [75], [76]. They have also been utilized for ship identification [51], [77], oil spill detection [60], and eddy detection [78], among other applications.…”

A Graph Memory Neural Network for Sea Surface Temperature Prediction

“…In fact, SST is a variable with spatiotemporal properties, showing dynamic and nonlinear characteristics. However, previous works overlook the spatial features of SST, which limits the prediction accuracy of SST [20]. To fully consider spatial information, researchers generally adopt two approaches.…”