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.

Name	Code	Latitude	Longitude
Paju	99	37.885	126.766
Seoul	108	37.571	126.965
Incheon	112	37.477	126.624
Suwon	119	37.272	126.985
Ganghwa	201	37.707	126.446
Yangpyeong	202	37.488	127.494
Gwanak	116	37.445	126.964
Gangnam	400	37.513	127.046
Gangseo	404	37.573	126.829
Gangbuk	424	37.639	127.025
Uijeongbu	532	37.734	127.073
Namyangju	541	37.634	127.150
Daeseongri	542	37.684	127.380
Gwangju	546	37.435	127.259
Yongin	549	37.270	127.221
Osan	550	37.187	127.048
Guri	569	37.582	127.156
Hwaseong	571	37.195	126.820
Yangju	598	37.831	126.990
Bupyeong	649	37.472	126.750