Name
Abstract | ||
|---|---|---|
This paper carries out a refinement on the basis of existing data sets, whose level of granularity is not available for some experimental analysis such as thermocline research. The thermocline is sensitive to thermohaline data granularity for sudden sea temperature changes. We reined the data with the KNN regression method and managed to choose the optimal parameters for the construction of a prediction model. We also refined the temperature and salinity data in BOA_Argo using the regression forecast model. The original data, whose horizontal resolution is 1 °x 1 °and vertically divided into uneven 58 layers from the sea surface to 1,975 meters underwater, has been refined into a new set with the resolution of 1 °x 1 °horizontally and 1-meter interval vertically. At each point, we reined the previously uneven 58 temperature data samples into 1,976 evenly distributed data samples. The refined data sets can be used in experimental analysis, and the validity of this method has been verified by regional data.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3291940.3291967 | WUWNet'18: The 13th ACM International Conference on Underwater Networks & Systems
Shenzhen
China
December, 2018 |
Field | DocType | ISBN |
Meteorology,Thermohaline circulation,Data set,Regression analysis,Computer science,Thermocline,Real-time computing,Argo,Granularity,Temperature salinity diagrams,Underwater | Conference | 978-1-4503-6193-4 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Gou | 1 | 0 | 0.34 |
| Jun Liu | 2 | 102 | 18.78 |
| Tong Zhang | 3 | 53 | 18.56 |