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Article

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

Department of Marine Environmental Informatics & Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2203; https://doi.org/10.3390/rs12142203
Received: 23 May 2020 / Revised: 23 June 2020 / Accepted: 8 July 2020 / Published: 9 July 2020
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 dataset combinations with radar reflectivity and ground meteorological attributes. Various ground meteorological attributes (such as relative humidity, wind speed, precipitation, etc.) were obtained using the land-based weather stations affiliated with Taiwan’s Central Weather Bureau (CWB). This study used nine radar reflectivity provided by the Hualien weather surveillance radar station’s Volume Cover Pattern 21 system. The developed models are built using multiple machine learning algorithms, including linear regression (REG), support vector regression (SVR), and extreme gradient boosting (XGBoost), in addition to the Marshall–Palmer formula (MP). The study examined 14 typhoons that occurred from 2008 to 2017 at Chenggong station in southeast Taiwan, and Lanyu station in the outlying islands, and the top four major rainfall events were designated as test typhoons—Nanmadol (2011), Tembin (2012), Matmo (2014), and Nepartak (2016). The results indicated that for rainfall retrievals, radar reflectivity at a scanning (elevation) angle of 6.0° combined with ground meteorological attributes were the optimal input variables for the Chenggong station, whereas radar reflectivity at an elevation angle of 4.3° combined with ground meteorological attributes were optimal for the Lanyu station. In terms of model performance, XGBoost models had the lowest error index at Chenggong and Lanyu stations compared with MP, REG, and SVR models. XGBoost models at Lanyu station had the highest efficiency coefficient (0.903), and those at Chenggong station had the second highest (0.885). As a result, pairing the combination of optimal radar reflectivity and ground meteorological attributes, as verified by the evaluation process, with a high-efficiency algorithm (XGBoost) can effectively increase the accuracy of rainfall retrieval during typhoons. View Full-Text
Keywords: rainfall estimation; radar reflectivity; machine learning; typhoon; modeling rainfall estimation; radar reflectivity; machine learning; typhoon; modeling
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MDPI and ACS Style

Wei, C.-C.; Hsu, C.-C. Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles. Remote Sens. 2020, 12, 2203. https://doi.org/10.3390/rs12142203

AMA Style

Wei C-C, Hsu C-C. Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles. Remote Sensing. 2020; 12(14):2203. https://doi.org/10.3390/rs12142203

Chicago/Turabian Style

Wei, Chih-Chiang, and Chen-Chia Hsu. 2020. "Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles" Remote Sensing 12, no. 14: 2203. https://doi.org/10.3390/rs12142203

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