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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
Joint Authors
Ro, Yonghun
Shin, Ju-Young
Cha, Joo-Wan
Kim, Kyu-Rang
Ha, Jong-Chul
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-07
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-based model.
The applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to quantitative precipitation estimation models was investigated using case studies with polarization radar data from Gwangdeoksan radar station.
Various combinations of input variable sets were tested, and results showed that machine learning algorithms can be applied to build the quantitative precipitation estimation model of the polarization radar data in South Korea.
The machine learning-based quantitative precipitation estimation models led to better performances than ZR relationship-based models, particularly for heavy rainfall events.
The extreme learning machine is considered the best of the algorithms used based on evaluation criteria.
American Psychological Association (APA)
Shin, Ju-Young& Ro, Yonghun& Cha, Joo-Wan& Kim, Kyu-Rang& Ha, Jong-Chul. 2019. 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. Advances in Meteorology،Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1118729
Modern Language Association (MLA)
Shin, Ju-Young…[et al.]. 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. Advances in Meteorology No. 2019 (2019), pp.1-17.
https://search.emarefa.net/detail/BIM-1118729
American Medical Association (AMA)
Shin, Ju-Young& Ro, Yonghun& Cha, Joo-Wan& Kim, Kyu-Rang& Ha, Jong-Chul. 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. Advances in Meteorology. 2019. Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1118729
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118729