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

<div>Leave-one-out cross-validation results of hyperparameters for the random forest (the number of trees), stochastic gradient boosted model (the number of trees), and extreme learning machine (the number of hidden nodes) models. The red circles indicate the selected optimal points of the employed hyperparameters based on the root-mean-square error. LOOCV results of (a) RF, (b) GBM, and (c) ELM.</div>

Advances in Meteorology

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Figure 2: 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 2 | 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 