[논문]시계열 분석 및 딥러닝 모형을 활용한 월 강수량 예측 비교

정연지; 김민기; 엄명진

doi:10.12652/ksce.2024.44.4.0443

[국내논문] 시계열 분석 및 딥러닝 모형을 활용한 월 강수량 예측 비교
Comparing Monthly Precipitation Predictions Using Time Series Analysis with Deep Learning Models

KSCE Journal of Civil and Environmental Engineering Research = 대한토목학회논문집, v.44 no.4, 2024년, pp.443 - 463

정연지 (경기대학교 토목공학과) , 김민기 (경기대학교 토목공학과) , 엄명진 (경기대학교 사회에너지시스템공학과)

초록
AI-Helper

본 연구는 지역별로 과거 30년간의 월 강수량 데이터를 활용하여 강수량 예측의 정확성을 높이고자 하였다. 통계적 모형(ARIMA, SARIMA)과 딥러닝 모형(LSTM, GBM)을 사용하여 강릉, 광주, 대구, 대전, 부산, 서울, 제주 그리고 춘천 지역에서 1983년부터 2012년까지의 월 강수량 데이터를 학습하고 이를 바탕으로 2013년부터 2022년까지 10년간 월 강수량을 예측하였다. 예측 결과, 대부분 모형에서 강수량의 추세는 정확하게 예측했으나, 실제 강수량보다 과소 예측하는 경향을 보였다. 이러한 문제점을 해결하고자 지역별, 계절별 적합한 모델을 선정하였다. 강릉, 광주, 대구, 대전, 부산, 서울, 제주 그리고 춘천에는 LSTM 모형이 적합한 결과를 보였다. 계절별로 나누어 예측력을 비교하면, SARIMA 모형은 강릉, 광주, 대구, 대전, 서울 그리고 춘천 지역에서 겨울철에 특히 적합한 예측 성능을 나타냈다. 또한, LSTM 모형은 강수가 집중된 여름철에 다른 모형에 비해 높은 성능을 보였다. 결론적으로, 지역별 및 계절별 강수 패턴을 면밀하게 분석하고 이에 기반한 적합한 예측 모형을 선택하는 것은 강수량 예측의 정확도를 높이는 데 결정적인 역할을 한다.

Abstract ▼ AI-Helper

This study sought to improve the accuracy of precipitation prediction by utilizing monthly precipitation data for each region over the past 30 years. Using statistical models (ARIMA, SARIMA) and deep learning models (LSTM, GBM), we learned monthly precipitation data from 1983 to 2012 in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. Based on this, monthly precipitation was predicted for 10 years from 2013 to 2022. As a result of the prediction, most models accurately predicted the precipitation trend, but showed a tendency to underpredict the actual precipitation. To solve these problems, appropriate models were selected for each region and season. The LSTM model showed suitable results in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. When comparing forecasting power by season, the SARIMA model showed particularly suitable forecasting performance in winter in Gangneung, Gwangju, Daegu, Daejeon, Seoul, and Chuncheon. Additionally, the LSTM model showed higher performance than other models in the summer when precipitation is concentrated. In conclusion, closely analyzing regional and seasonal precipitation patterns and selecting the optimal prediction model based on this plays a critical role in increasing the accuracy of precipitation prediction.

Keyword

표/그림 (19)

그림 Fig. 1. Concept of Recurrent Neural Network Structure (Lukic, 2020)
그림 Fig. 2. Structure of LSTM (Yang et al., 2020)
그림 Fig. 3. Process of the LightGBM Model (Choi et al., 2023)
그림 Fig. 4. Weather Stations in This Study
그림 Fig. 5. Observed Monthly Precipitation(1983~2022). (a) Gangneung, (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
그림 Fig. 6. ARIMA Model Hyperparameter(p, d, q). (a) Gangneung, (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
그림 Fig. 7. Predicted Monthly Precipitation Using ARIMA(2013~2022). (a) Gangneung (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
그림 Fig. 8. SARIMA Model Hyperparameter(p, d, q, P, D, Q). (a) Gangneung, (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
그림 Fig. 9. Predicted Monthly Precipitation Using SARIMA(2013~2022). (a) Gangneung, (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
그림 Fig. 10. Optimal Learning Rate, Batch Sizes, and Epochs for LSTM Models by Stations and Year
표 Table 1. LSTM Hyper Parameter
그림 Fig. 11. Predicted Monthly Precipitation Using LSTM(2013~2022). (a) Gangneung, (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
그림 Fig. 12. Optimal Learning Rate and n_estimators for GBM Models by Stations and Year
표 Table 2. GBM Hyper Parameter
그림 Fig. 13. Predicted Monthly Precipitation Using GBM(2013~2022). (a) Gangneung, (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
그림 Fig. 14. Comparison between Predicted Precipitation and Observed Precipitation with Each Method. (a) Gangneung, (b) Gwangju, (c) Daegu, (d) Daejeon, (e) Busan, (f) Seoul, (g) Jeju, (h) Chuncheon
표 Table 3. Accuracy Evaluation for each 8 Observed Location Model
표 Table 4. Seasonal Accuracy Evaluation for each 8 Observed Location Model
표 Table 4. Seasonal Accuracy Evaluation for each 8 Observed Location Model (Continued)

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