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http://dx.doi.org/10.5391/JKIIS.2008.18.3.297

Combining Radar and Rain Gauge Observations Utilizing Gaussian-Process-Based Regression and Support Vector Learning  

Yoo, Chul-Sang (고려대학교 건축사회환경공학과)
Park, Joo-Young (고려대학교 제어계측공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.3, 2008 , pp. 297-305 More about this Journal
Abstract
Recently, kernel methods have attracted great interests in the areas of pattern classification, function approximation, and anomaly detection. The role of the kernel is particularly important in the methods such as SVM(support vector machine) and KPCA(kernel principal component analysis), for it can generalize the conventional linear machines to be capable of efficiently handling nonlinearities. This paper considers the problem of combining radar and rain gauge observations utilizing the regression approach based on the kernel-based gaussian process and support vector learning. The data-assimilation results of the considered methods are reported for the radar and rain gauge observations collected over the region covering parts of Gangwon, Kyungbuk, and Chungbuk provinces of Korea, along with performance comparison.
Keywords
Gaussian process; Support vector learning; Kernel method; Function approximation; Data assimilation; Hydrology;
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Times Cited By KSCI : 4  (Citation Analysis)
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