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http://dx.doi.org/10.3741/JKWRA.2014.47.11.1095

Spatial Downscaling of Grid Precipitation Using Support Vector Machine Regression  

Moon, Heewon (Dept. of Civil, Architectural and Environmental System Engineering, Sungkyunkwan Univ.)
Baik, Jongjin (Dept. of Civil, Architectural and Environmental System Engineering, Sungkyunkwan Univ.)
Hwang, Sukhwan (Water Resources Research Division, Korea Institute of Civil Engineering and Building Technology)
Choi, Minha (Dept. of Water Resources, Graduate School of Water Resources, Sungkyunkwan Univ.)
Publication Information
Journal of Korea Water Resources Association / v.47, no.11, 2014 , pp. 1095-1105 More about this Journal
Abstract
A spatial downscaling method using the Support Vector Machine (SVM) Regression for 25 km Tropical Rainfall Measuring Mission (TRMM) Monthly precipitation is proposed. The nonlinear relationship among hydrometeorological variables and precipitation was effectively depicted by the SVM for predicting downscaled grid precipitation. The accuracy of spatially downscaled precipitation was estimated by comparing with rain gauge data from sixty-four stations and found to be improved than the original TRMM data in overall. Especially the positive bias of the original TRMM data was effectively removed after the downscaling procedure. The spatial distributions of 25 km and 1 km grid precipitation were generally similar, while the local spatial trend was better detected by 1 km grid precipitation. The downscaled grid data derived from the proposed method can be applied in hydrological modelling for higher accuracy and further be studied for developing optimized downscaling method incorporation other regression methods.
Keywords
TRMM 3B43 V7; SVM; downscaling; ASOS;
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Times Cited By KSCI : 2  (Citation Analysis)
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