한국농공학회지 (Magazine of the Korean Society of Agricultural Engineers)

한국농공학회 (The Korean Society of Agricultural Engineers)
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수문기학적 자연재해 경감을 위한 Al의 적용

Mitigation of Hydrometeorological Hazards using AI techniques

  • Jang, Won-Seok (University of Colorado Boulder Sustainability Innovation Lab at Colorado (SILC)) ;
  • Neff, Jason (University of Colorado Boulder Sustainability Innovation Lab at Colorado (SILC))
  • 발행 : 2019.08.31
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참고문헌

  1. 정하명, 2013. 기후변화 대응을 위한 재난재해 관련 법제에 관한 연구. 법제처.
  2. A. Mercer, J. Dyer, 2014. A new scheme for daily peak wird gust prediction using machine learning. Proceda Computer Science 36: 593-598. https://doi.org/10.1016/j.procs.2014.09.059
  3. C. Adam-Bourdarios, G. Cowan, C. Germain-Renaud, I. Guyon, B. Kegl, D. Rousseau, 2015. The Higgs machine learning challenge. J. Phys. Conf. Ser. 664, 72015. https://doi.org/10.1088/1742-6596/664/7/072015
  4. H. Nielsen, H. Madsen, and T. Nielsen, 2006. Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts. Wind Energy 9: 95-108. https://doi.org/10.1002/we.180
  5. H. Wu, M. Huang, Q. Tang, D. B. Kirschbaum, P. Ward, 2016. Hydrometeorological Hazards: Monitoring, Forecasting, Risk Assessment, and Socioeconomic Responses. Advances in Meteorology 2016: 1-3.
  6. J. Noymance, N. O. Nikitin, A. V. Kalyuzhnaya, Urban pluvial flood forecasting using open data with machine learning techniques in Pattani Basin. Procedia Computer Science, 119: 288-297. https://doi.org/10.1016/j.procs.2017.11.187
  7. L. Bao, T. Gneitlng, E. Grimit, P. Guttorp, A. Raftery, 2010. Bias correction and Bayesian model averaging tor ensemble forecasts at surface wind direction. Mon. Wea. Rev. 138: 1811-1821. https://doi.org/10.1175/2009MWR3138.1
  8. P. Pinson, 2012. Adaptive calibartion of (u,V)-wind ensemble forecasts. Quart. J. Roy. Meteor. Soc. 138: 1273-1284. https://doi.org/10.1002/qj.1873
  9. R. Zhang, Z.-H. Chen, L.-J. Xu, C.-O. Qu, 2019. Meteorological drought forecasting based on a statistical model with machine learning techniques in Shaanxi province, China. Science of Total Environmental 665: 338-346. https://doi.org/10.1016/j.scitotenv.2019.01.431