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.
Model
Input variables
Z
Z, DR
Z, KD
DR, KD
Z, DR, KD
1
ZR-L1
11.97
11.91
11.98
18.12
11.92
ZR-L0
14.59
14.62
14.62
17.86
14.66
RF
11.29
11.30
11.01
15.52
11.20
GBM
11.05
11.05
11.03
16.33
11.06
ELM
10.30
10.11
10.21
13.12
10.06
2
ZR-L1
5.39
5.26
5.40
7.00
5.30
ZR-L0
6.13
5.99
6.15
7.51
6.00
RF
5.25
5.16
5.14
6.34
5.10
GBM
5.08
5.16
5.07
6.53
5.15
ELM
4.93
5.17
4.71
5.99
5.05
3
ZR-L1
3.33
3.33
3.33
3.57
3.33
ZR-L0
3.34
3.33
3.34
3.57
3.33
RF
3.47
3.17
3.43
3.54
3.20
GBM
3.35
3.31
3.35
3.48
3.32
ELM
3.64
3.91
3.75
3.85
3.99
4
ZR-L1
2.88
2.86
2.88
3.24
2.86
ZR-L0
3.05
3.02
3.05
3.26
3.02
RF
2.93
2.97
2.89
3.39
2.85
GBM
2.89
2.86
2.88
3.22
2.86
ELM
2.91
2.90
2.90
3.08
2.89
Italicized numbers indicate the smallest RMSEs among those calculated during the same rainfall events.
