Advances in Meteorology / 2019 / Article / Fig 2

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 2

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.
(a)
(b)
(c)

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.