DOI QR코드

DOI QR Code

Prediction of Temperature and Heat Wave Occurrence for Summer Season Using Machine Learning

기계학습을 활용한 하절기 기온 및 폭염발생여부 예측

  • Kim, Young In (Dept. of Civil Engineering, Hongik Univ.) ;
  • Kim, DongHyun (Dept. of Civil Engineering, Hongik Univ.) ;
  • Lee, Seung Oh (Dept. of Civil Engineering, Hongik Univ.)
  • 김영인 (홍익대학교 토목공학과) ;
  • 김동현 (홍익대학교 토목공학과) ;
  • 이승오 (홍익대학교 토목공학과)
  • Received : 2020.04.15
  • Accepted : 2020.06.29
  • Published : 2020.06.30

Abstract

Climate variations have become worse and diversified recently, which caused catastrophic disasters for our communities and ecosystem including economic property damages in Korea. Heat wave of summer season is one of causes for such damages of which outbreak tends to increase recently. Related short-term forecasting information has been provided by the Korea Meteorological Administration based on results from numerical forecasting model. As the study area, the ◯◯ province was selected because of the highest mortality rate in Korea for the past 15 years (1998~2012). When comparing the forecasted temperatures with field measurements, it showed RMSE of 1.57℃ and RMSE of 1.96℃ was calculated when only comparing the data corresponding to the observed value of 33℃ or higher. The forecasting process would take at least about 3~4 hours to provide the 4 hours advanced forecasting information. Therefore, this study proposes a methodology for temperature prediction using LSTM considering the short prediction time and the adequate accuracy. As a result of 4 hour temperature prediction using this approach, RMSE of 1.71℃ was occurred. When comparing only the observed value of 33℃ or higher, RMSE of 1.39℃ was obtained. Even the numerical prediction model of the whole range of errors is relatively smaller, but the accuracy of prediction of the machine learning model is higher for above 33℃. In addition, it took an average of 9 minutes and 26 seconds to provide temperature information using this approach. It would be necessary to study for wider spatial range or different province with proper data set in near future.

References

  1. Ahn, J. and Jeong, C. (2018). Numerical Simulation of the Flood Event Induced Temporally and Spatially Concentrated Rainfall - On August 17, 2017, the Flood Event of Cheonggyecheon. Journal of Korean Society of Disaster and Security. 11(2): 45-52. https://doi.org/10.21729/KSDS.2018.11.2.45
  2. Ahn, S. (2016). Deep Learning Architectures and Applications. Journal of Intelligence and Information Systems. 22(2): 127-142. https://doi.org/10.13088/jiis.2016.22.2.127
  3. Choi, J. (2019). Proposal of Early-Warning Criteria for Highway Debris Flow Using Rainfall Frequency (1): Proposal of Rainfall Criteria. Journal of Korean Society of Disaster and Security. 12(2): 1-13. https://doi.org/10.21729/KSDS.2019.12.2.1
  4. Choi, M. H. and Yun, J. I. (2009). On Recent Variations in Solar Radiation and Daily Maximum Temperature in Summer. Korean Journal of Agricultural and Forest Meteorology. 11(4): 185-191. https://doi.org/10.5532/KJAFM.2009.11.4.185
  5. Joints of Related Ministries (2018) Abnormal Weather Report. Seoul: Korea Meteorological Administration.
  6. Kim, J., Lee, D. G., Park, I. S., Choi, B. C., and Kim, J. S. (2006). Influences of Heat Waves on Daily Mortality in South Korea. Atmosphere. 16(4): 269-278.
  7. Korea Meteorological Administration (2018). Evaluation of Contribution of Meteorological Observation Data to Weather Forecasts. Seoul: Korea Meteorological Administration.
  8. Korea Meteorological Administration (2019). Forecast Services Regulations APPENDUM Article17. Seoul: Korea Meteorological Administration.
  9. Korea Meteorological Administration (2019). A Study on the Diagnosis and Development Direction of the Forecasting System. Seoul: Korea Meteorological Administration.
  10. Lee, S. G., Jung, S. G., Lee, W. S., and Park, G. H. (2011). A Predictive Model for Urban Temperature Using the Artificial Neural Network. Korea Planners Association. 46(1): 129-142.
  11. Park, J.E., Heo, B.Y., and Sunwoo, Y. (2016). A Study on Human Damage due to Heat Wave by Region. Journal of the Korean Society of Hazard Mitigation. 16(1): 103-109. https://doi.org/10.9798/KOSHAM.2016.16.1.103
  12. Qing, X. and Niu, Y. (2018). Hourly Day-ahead Solar Irradiance Prediction Using Weather Forecasts by LSTM. Energy. 148: 461-468. https://doi.org/10.1016/j.energy.2018.01.177
  13. Sharma, N., Sharma, P., Irwin, D., and Shenoy, P. (2011). Predicting Solar Generation from Weather Forecasts Using Machine Learning. In 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm). 528-533.
  14. Won, Y. J., Yeh, S. W., Yim, B. Y., and Kim, H. K. (2017). Relationship between Korean Monthly Temperature during Summer and Eurasian Snow Cover during Spring. Atmosphere. 27(1): 55-65. https://doi.org/10.14191/Atmos.2017.27.1.055
  15. Yoo, H., Lee, S. O., Choi, S., and Park, M. (2019). A Study on the Data Driven Neural Network Model for the Prediction of Time Series Data: Application of Water Surface Elevation Forecasting in Hangang River Bridge. Journal of Korean Society of Disaster and Security. 12(2): 73-82. https://doi.org/10.21729/KSDS.2019.12.2.73
  16. Yoon, J.W. and Jeon, M.G. Temperature Forecasting Model by Using Deep Learning Technology based on LSTM. Proceeding of Institute of Electronics and Information Engineers Conference. 912-915.
  17. Zhang, Q., Wang, H., Dong, J., Zhong, G., and Sun, X. (2017). Prediction of Sea Surface Temperature Using Long Short-term Memory. IEEE Geoscience and Remote Sensing Letters. 14(10): 1745-1749. https://doi.org/10.1109/LGRS.2017.2733548