Advances in Meteorology / 2019 / Article / Tab 5

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 5

Correlations of rain rate estimations by quantitative precipitation estimation models for each selected rainfall event.

Event no.ModelInput variables
ZZ, DRZ, KDDR, KDZ, DR, KD

1ZR-L10.7500.7530.7490.1540.752
ZR-L00.6030.6000.6000.2010.597
RF0.7850.7850.8000.5130.796
GBM0.7990.7990.8000.4250.799
ELM0.8290.8370.8300.6870.836

2ZR-L10.7210.7360.7210.4550.732
ZR-L00.6150.6390.6110.3320.637
RF0.7450.7500.7490.5930.756
GBM0.7580.7490.7590.5590.750
ELM0.7930.7850.8050.6490.788

3ZR-L10.3080.3090.3080.0070.308
ZR-L00.2870.2920.286−0.0560.292
RF0.2280.4230.2300.2080.404
GBM0.2980.3620.2980.2070.358
ELM0.3640.4170.3550.3710.389

4ZR-L10.4180.4310.4190.0470.429
ZR-L00.2750.2990.279−0.0650.298
RF0.4330.4180.4490.0180.459
GBM0.4220.4350.423−0.0040.433
ELM0.3950.4090.4000.2580.415

Italicized numbers indicate the largest correlations among those calculated during the same rainfall events.

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