@inproceedings{5777c8c23a4248168f8382cedd5097e6,
title = "KNN regression model-based refinement of thermohaline data",
abstract = "This paper carries out a renement 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 rened the data with the KNN regression method and managed to choose the optimal parameters for the construction of a prediction model. We also rened 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 rened into a new set with the resolution of 1 °x 1 °horizontally and 1-meter interval vertically. At each point, we rened the previously uneven 58 temperature data samples into 1,976 evenly distributed data samples. The rened data sets can be used in experimental analysis, and the validity of this method has been veried by regional data.",
keywords = "BOA\_ARGO, Granularity, KNN, Thermocline, Thermohaline data",
author = "Yu Gou and Jun Liu and Tong Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 13th ACM International Conference on Underwater Networks and Systems, WUWNet 2018 ; Conference date: 03-12-2018 Through 05-12-2018",
year = "2018",
month = dec,
day = "3",
doi = "10.1145/3291940.3291967",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
booktitle = "Proceedings of the 13th ACM International Conference on Underwater Networks and Systems, WUWNet 2018",
address = "美国",
}