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http://dx.doi.org/10.15207/JKCS.2020.11.8.007

Construction of Super-Resolution Convolutional Neural Network Model for Super-Resolution of Temperature Data  

Kim, Yong-Hoon (Department of Computer Science, Kwangwoon University)
Im, Hyo-Hyuk (Korea Oceanic & Atmospheric System Technology)
Ha, Ji-Hun (IT Division, Korea Oceanic & Atmospheric System Technology)
Park, Kun-Woo (IT Division, Korea Oceanic & Atmospheric System Technology)
Kim, Yong-Hyuk (Department of Computer Science, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.8, 2020 , pp. 7-13 More about this Journal
Abstract
Meteorology and climate are closely related to human life. By using high-resolution weather data, services that are useful for real-life are available, and the need to produce high-resolution weather data is increasing. We propose a method for super-resolution temperature data using SRCNN. To evaluate the super-resolution temperature data, the temperature for a non-observation point is obtained by using the inverse distance weighting method, and the super-resolution temperature data using interpolation is compared with the super-resolution temperature data using SRCNN. We construct an SRCNN model suitable for super-resolution of temperature data and perform super-resolution of temperature data. As a result, the prediction performance of the super-resolution temperature data using SRCNN was about 10.8% higher than that using interpolation.
Keywords
Temperature data; Super-resolution; Machine learning; SRCNN; Inverse distance weighting; Interpolation;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
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