Advances in Meteorology / 2019 / Article / Tab 6

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 6

Relative root-mean-square errors (RRMSEs) 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-L185.585.185.6129.585.2
ZR-L0104.3104.5104.5127.6104.7
RF81.180.278.5112.279.8
GBM78.879.079.1116.879.2
ELM73.572.172.993.571.8

2ZR-L177.875.977.9101.076.4
ZR-L088.586.488.8108.486.6
RF76.474.074.591.673.6
GBM73.174.673.294.174.5
ELM71.275.068.186.572.6

3ZR-L163.663.763.668.363.7
ZR-L063.863.863.868.363.8
RF66.360.665.667.460.8
GBM64.163.464.166.663.4
ELM69.675.871.973.376.9

4ZR-L163.563.063.571.463.0
ZR-L067.466.767.372.066.7
RF64.765.364.175.063.0
GBM63.663.063.571.463.0
ELM64.263.863.868.063.4

Italicized numbers indicate the smallest RRMSEs among those calculated during the same rainfall events.

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