Surface Rainfall Estimation Based on Radar Image Analysis and Fully Connected Neural Network

홈 > 연구문헌 > 학술대회 프로시딩 > 한국정보과학회 학술대회 > 2018년 컴퓨터종합학술대회

한글제목(Korean Title)	Surface Rainfall Estimation Based on Radar Image Analysis and Fully Connected Neural Network
영문제목(English Title)	Surface Rainfall Estimation Based on Radar Image Analysis and Fully Connected Neural Network
저자(Author)	Oudomseila Phok Jiwan Lee Bonghee Hong
원문수록처(Citation)	VOL 45 NO. 01 PP. 0110 ~ 0112 (2018. 06)
한글내용 (Korean Abstract)	The weather radar images, which produce by the Radar Weather Station (RWS) represent the intensity of the rainfall through the color pixel, which has different color based on different rain intensity. The goal of this paper is used the provided radar weather images and a learning model to estimate the surface rain of CCTV Locations on the road at a given time. Radar image data from surrounding area will be analyzed first through correlation analysis to select the best three candidates. Then this data set is combined with the surface rain data, windspeed, temperature, humid and local pressure from Automated Synoptic Observing System (ASOS), to generate a prediction model.
영문내용 (English Abstract)
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