[논문]기계학습의 LSTM을 적용한 지상 기상변수 예측모델 개발

홍성재; 김재환; 최대성; 백강현

doi:10.14191/atmos.2021.31.1.073

[국내논문] 기계학습의 LSTM을 적용한 지상 기상변수 예측모델 개발
Development of Surface Weather Forecast Model by using LSTM Machine Learning Method 원문보기

대기 = Atmosphere, v.31 no.1, 2021년, pp.73 - 83

홍성재 (부산대학교 대기과학과) , 김재환 (부산대학교 대기과학과) , 최대성 (부산대학교 대기과학과) , 백강현 (부산대학교 기후연구센터)

Abstract ▼ AI-Helper

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.

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표/그림 (10)

표 Table 1. The information of 9 ASOS station used in LSTM model.
그림 Fig. 1. Flowchart of LSTM memory cell.
그림 Fig. 2. (a) underfitting, (b) just right fitting, (c) overfitting as training continues (http://sanghyukchun.github.io/59/).
그림 Fig. 3. Training and validation loss (MAE) at Seoul station with, (a) none dropout coefficient, (b) 0.2 dropout coefficient.
그림 Fig. 4. The shape of activation functions, (a) sigmoid, (b) tanh, and (c) ReLU.
표 Table 2. List of features input in five experiments.
그림 Fig. 5. Station averaged RMSE between LSTM and ASOS by 5 experiments in 2019, (a) temperature, (b) relative humidity, and (c) wind speed.
그림 Fig. 6. Station averaged RMSE between LSTM and ASOS by each activation functions in 2019, (a) temperature, (b) relative humidity, and (c) wind speed.
그림 Fig. 7. Monthly RMSE between LSTM and ASOS temperature (blue line), relative humidity (green line), and wind speed (red line).
그림 Fig. 8. Comparison of RMSE between LSTM (solid line) and LDAPS (dash line) temperature (red), wind speed (purple), and RH (blue).

참고문헌 (21)

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