2022
DOI: 10.3390/s22041636
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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 Tem… Show more

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Cited by 15 publications
(9 citation statements)
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References 25 publications
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“…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…”
Section: Resultsmentioning
confidence: 99%
“…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…”
Section: Resultsmentioning
confidence: 99%
“…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.…”
Section: Interpretabilitymentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%