Advances in Meteorology / 2019 / Article / Tab 4

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 4

Root-mean-square errors (RMSEs) of rainfall rates estimated by quantitative precipitation estimation models for selected rainfall events.

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

1ZR-L111.9711.9111.9818.1211.92
ZR-L014.5914.6214.6217.8614.66
RF11.2911.3011.0115.5211.20
GBM11.0511.0511.0316.3311.06
ELM10.3010.1110.2113.1210.06

2ZR-L15.395.265.407.005.30
ZR-L06.135.996.157.516.00
RF5.255.165.146.345.10
GBM5.085.165.076.535.15
ELM4.935.174.715.995.05

3ZR-L13.333.333.333.573.33
ZR-L03.343.333.343.573.33
RF3.473.173.433.543.20
GBM3.353.313.353.483.32
ELM3.643.913.753.853.99

4ZR-L12.882.862.883.242.86
ZR-L03.053.023.053.263.02
RF2.932.972.893.392.85
GBM2.892.862.883.222.86
ELM2.912.902.903.082.89

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

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