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학술대회 A Study on Rainfall Prediction based on Meteorological Time Series
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저자
홍강운, 강태규
발행일
202108
출처
International Conference on Ubiquitous and Future Networks (ICUFN) 2021, pp.1-3
DOI
https://dx.doi.org/10.1109/ICUFN49451.2021.9528816
협약과제
20VR1800, AI기술을 활용한 공공데이터 기반 지역현안 솔루션 개발 및 실용화 - 세부4 ETRI 인공지능 신산업 거점육성 테스트베드 구축, 강태규
초록
This study aims to present the results of the research and development project on the urban inundation prediction technology during the heavy rain period. In this study, the results of rainfall prediction using heterogeneous weather data and machine learning are presented. In the predictive analysis of univariate time series data, it was confirmed that the CNN-LSTM model showed the best performance among several deep neural network models. In the predictive analysis of multivariate time series data, it was confirmed that the ConvLSTM model showed the best performance among several deep neural network models.
키워드
deep neural network, meteorological data, rainfall prediction
KSP 제안 키워드
Best performance, Deep neural network(DNN), Development Project, Heavy rain, Meteorological data, Multivariate time series, Prediction technology, Time series data, Univariate time series, machine Learning, neural network model