Advances in Meteorology / 2019 / Article / Tab 1

Research Article

Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

Table 1

Ground precipitation gauge stations selected for this study.

NameCodeLatitudeLongitude

Paju9937.885126.766
Seoul10837.571126.965
Incheon11237.477126.624
Suwon11937.272126.985
Ganghwa20137.707126.446
Yangpyeong20237.488127.494
Gwanak11637.445126.964
Gangnam40037.513127.046
Gangseo40437.573126.829
Gangbuk42437.639127.025
Uijeongbu53237.734127.073
Namyangju54137.634127.150
Daeseongri54237.684127.380
Gwangju54637.435127.259
Yongin54937.270127.221
Osan55037.187127.048
Guri56937.582127.156
Hwaseong57137.195126.820
Yangju59837.831126.990
Bupyeong64937.472126.750

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