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.
Model
Input variables
Z
Z, DR
Z, KD
DR, KD
Z, DR, KD
1
ZR-L1
0.750
0.753
0.749
0.154
0.752
ZR-L0
0.603
0.600
0.600
0.201
0.597
RF
0.785
0.785
0.800
0.513
0.796
GBM
0.799
0.799
0.800
0.425
0.799
ELM
0.829
0.837
0.830
0.687
0.836
2
ZR-L1
0.721
0.736
0.721
0.455
0.732
ZR-L0
0.615
0.639
0.611
0.332
0.637
RF
0.745
0.750
0.749
0.593
0.756
GBM
0.758
0.749
0.759
0.559
0.750
ELM
0.793
0.785
0.805
0.649
0.788
3
ZR-L1
0.308
0.309
0.308
0.007
0.308
ZR-L0
0.287
0.292
0.286
−0.056
0.292
RF
0.228
0.423
0.230
0.208
0.404
GBM
0.298
0.362
0.298
0.207
0.358
ELM
0.364
0.417
0.355
0.371
0.389
4
ZR-L1
0.418
0.431
0.419
0.047
0.429
ZR-L0
0.275
0.299
0.279
−0.065
0.298
RF
0.433
0.418
0.449
0.018
0.459
GBM
0.422
0.435
0.423
−0.004
0.433
ELM
0.395
0.409
0.400
0.258
0.415
Italicized numbers indicate the largest correlations among those calculated during the same rainfall events.
