Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 14; p. 2203
• Main Authors: Wei, Chih-Chiang, Hsu, Chen-Chia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2020
• Subjects: Algorithms; Angle of reflection; Artificial intelligence; Atmospheric models; Elevation angle; Emergency preparedness; Hydrology; Learning algorithms; Machine learning; modeling; Neural networks; Precipitation; Radar; radar reflectivity; Rain; Rainfall; rainfall estimation; Rainfall rate; Reflectance; Relative humidity; Remote sensing; Retrieval; Studies; Support vector machines; Surveillance radar; typhoon; Typhoons; Weather; Weather stations; Wind speed; Taiwan

The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various datase...