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.
Model
Input variables
Z
Z, DR
Z, KD
DR, KD
Z, DR, KD
1
ZR-L1
85.5
85.1
85.6
129.5
85.2
ZR-L0
104.3
104.5
104.5
127.6
104.7
RF
81.1
80.2
78.5
112.2
79.8
GBM
78.8
79.0
79.1
116.8
79.2
ELM
73.5
72.1
72.9
93.5
71.8
2
ZR-L1
77.8
75.9
77.9
101.0
76.4
ZR-L0
88.5
86.4
88.8
108.4
86.6
RF
76.4
74.0
74.5
91.6
73.6
GBM
73.1
74.6
73.2
94.1
74.5
ELM
71.2
75.0
68.1
86.5
72.6
3
ZR-L1
63.6
63.7
63.6
68.3
63.7
ZR-L0
63.8
63.8
63.8
68.3
63.8
RF
66.3
60.6
65.6
67.4
60.8
GBM
64.1
63.4
64.1
66.6
63.4
ELM
69.6
75.8
71.9
73.3
76.9
4
ZR-L1
63.5
63.0
63.5
71.4
63.0
ZR-L0
67.4
66.7
67.3
72.0
66.7
RF
64.7
65.3
64.1
75.0
63.0
GBM
63.6
63.0
63.5
71.4
63.0
ELM
64.2
63.8
63.8
68.0
63.4
Italicized numbers indicate the smallest RRMSEs among those calculated during the same rainfall events.
