Advances in Meteorology / 2019 / Article / Fig 8

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 8

Radar rainfall rate fields of four selected quantitative precipitation estimation models (ZR2–L1, RF5, GBM2, and ELM5) for event #4 (November 8, 2018; 12 : 40). (a) ZR2-L1. (b) RF5. (c) GBM2. (d) ELM5.

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 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.