Advances in Meteorology / 2019 / Article / Fig 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

Figure 4

Plots of rainfall rate estimation versus observations for the models tested for all precipitation events studied. (a) ZR1-L1 (RMSE: 8.75, R: 0.58). (b) ZR2-L1 (RMSE: 8.87, R: 0.56). (c) ZR3-L1 (RMSE: 8.74, R: 0.58). (d) ZR5-L1 (RMSE: 8.86, R: 0.58). (e) ZR1-L0 (RMSE: 9.33, R: 0.47). (f) ZR2-L0 (RMSE: 9.36, R: 0.46). (g) ZR3-L0 (RMSE: 9.35, R: 0.47). (h) ZR5-L0 (RMSE: 9.37, R: 0.46). (i) RF1 (RMSE: 8.56, R: 0.6). (j) RF2 (RMSE: 8.26, R: 0.63). (k) RF3 (RMSE: 8.36, R: 0.62). (l) RF5 (RMSE: 8.18, R: 0.63). (m) GBM1 (RMSE: 8.38, R: 0.61). (n) GBM2 (RMSE: 8.43, R: 0.61). (o) GBM3 (RMSE: 8.38, R: 0.24). (p) GBM5 (RMSE: 8.43, R: 0.6). (q) ELM1 (RMSE: 8.37, R: 0.64). (r) ELM2 (RMSE: 7.99, R: 0.66). (s) ELM3 (RMSE: 8.33, R: 0.64). (t) ELM5 (RMSE: 7.91, R: 0.67).
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