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입력 데이터 해상도에 따른 심층학습 알고리즘의 기상 변수 예측 정확도 평가Assessing the Accuracy of Deep Learning Algorithms in Predicting Weather Variables According to Input Data Resolution

Other Titles
Assessing the Accuracy of Deep Learning Algorithms in Predicting Weather Variables According to Input Data Resolution
Authors
서상익이창준기서진
Issue Date
2022
Publisher
한국환경기술학회
Keywords
Deep learning; Temporal resolution; Weather variables; Multi layer perceptron; Long short-term memory; Priority ranking
Citation
한국환경기술학회지, v.23, no.1, pp 16 - 21
Pages
6
Indexed
KCI
Journal Title
한국환경기술학회지
Volume
23
Number
1
Start Page
16
End Page
21
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/2497
ISSN
1229-8425
2635-7437
Abstract
We evaluated the performance of deep learning algorithms predicting air temperature in different time steps. Three different data sets were compiled at various time intervals covering days, hours, and minutes for three separate months (i.e., January, July, and November 2021) in two monitoring stations (i.e., one in Seoul 108 and the other in Jinju 192) from the Korea Meteorological Administration. Those data sets divided into 70 % for training and 30 % for testing were provided as inputs to two popular algorithms, the multi layer perceptron (MLP) and long short-term memory (LSTM). Our results showed that the MLP algorithm exhibited superior prediction performance for data recorded at one-minute intervals rather than those updated hourly or daily. In addition, the MLP algorithm was found to work best for data with seasonality. The predictive accuracy was, however, slightly lower for the MLP algorithm than for the LSTM algorithm which yielded error rates as low as 0.04 in terms of the mean absolute error. All these results implied that the use of high-frequency data played an important role in improving the performance of deep learning as well as the proposed methodology could be used to prioritize candidate algorithms with input data (resolution) for prediction of weather variables.
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Ki, Seo Jin
건설환경공과대학 (환경공학과)
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