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2022 Vol.15, Issue 4 Preview Page
31 December 2022. pp. 87-98
Ghada, W., Eastrella, N., and Meanzel, A. (2019). Machine Learning Approach to Classify Rain Type based on This Disdrometers and Cloud Observations. Atmosphere. 10(5): 251-268. 10.3390/atmos10050251
Hong, W. C. (2008). Rainfall Forecasting by Technological Machine Learning Models. AMC, 200(1): 41-57. 10.1016/j.amc.2007.10.046
Kim, S. H., Kim, H. M., Kay, J. K., and Lee, S. W. (2015). Development and Evaluation of the High Resolution Limited Area Ensemble Prediction System in the Korea Meteorological Administration. Korean Meteorological Society. 25(1): 67-83. 10.14191/Atmos.2015.25.1.067
Ko, C. M., Jeong, Y. Y., Ji, Y. K., Lee, Y. M., and Kim, B. S. (2020). A Study on Hydrological Rainfall Adjustment using Machine Learning and Probability Matching Method during Heavy Rainfall Season. Journal of Climate Research. 15(4): 257-267. 10.14383/cri.2020.15.4.257
Lee, H. S., Jee, Y. K., Lee, Y. M., and Kim, B. S. (2021). Optimizing Hydrological Quantitative Precipitation Forecast (HQPF) based on Machine Learning for Rainfall Impact Forecasting. Journal of Environmental Science International. 30(12): 891-905. 10.5322/JESI.2021.30.12.1053
Lee, S. H., Kang, D. H., and Kim, B. S. (2018). A Study on the Method of Calculating the Threshold Rainfall for Rainfall Impact Forecasting. Journal of the Korean Society of Hazard Mitigation. 18(7): 93-102. 10.9798/KOSHAM.2018.18.7.93
Roberts, N. M. (2008). Assessing the Spatial and Temporal Variation in Skill of Precipitation Forecasts from an NWP Model. Meteorological Applications. 15(1): 163-169. 10.1002/met.57
Sumi, S. M., Zaman, M. F., and Hirose, H. (2012). A Rainfall Forecasting Method using Machine Learning Models and its Application to the Fukuoka City Case. Int. J. Appl. Math. Comput. Sci. 22(4): 841-854. 10.2478/v10006-012-0062-1
Valipour, M., Sefidkouhi, G., Ali, M., Raeini-Sarjaz, M., and Guzman, S. M. (2019). A Hybrid Data-driven Machine Learning Technique for Evapotranspiration Modeling Various Climates. Atmosphere. 10(6): 311-325. 10.3390/atmos10060311
Zamami, J. M., Cao, C., Ni, X., Bashir, B., and Talebiesfandarani, S. (2019). PM2.5 Prediction based on Random Forest, XGBoost, and Deep Learning using Multisource Remote Sensing Data. Atmosphere. 10(7): 373-391. 10.3390/atmos10070373

Korean References Translated from the English

고철민, 정영윤, 지용근, 이영미, 김병식 (2020). 집중호우 시기 기계학습 및 PM기법을 이용한 수문학적 강우보정에 관한 연구. 건국대학교 기후연구소. 15(4): 257-267. 10.14383/cri.2020.15.4.257
김세현, 김현미, 계준경, 이승우 (2015). 기상청 고해상도 국지 앙상블 예측 시스템 구축 및 성능 검증. 한국기상학회. 25(1): 67-83. 10.14191/Atmos.2015.25.1.067
이석호, 강동호, 김병식 (2018). 호우영향예보를 위한 한계강우량 산정 방법 연구. 한국방재학회논문집. 18(7): 93-102.
이한수, 지용근, 이영미, 김병식 (2021). 호우 영향예보를 위한 머신러닝 기반의 수문학적 정량강우예측(HQPF) 최적화 방안. 한국환경과학회지. 30(12): 891-905.
  • Publisher :Korean Society of Disaster and Security
  • Publisher(Ko) :한국방재안전학회
  • Journal Title :Journal of Korean Society of Disaster and Security
  • Journal Title(Ko) :한국방재안전학회 논문집
  • Volume : 15
  • No :4
  • Pages :87-98
  • Received Date :2022. 12. 08
  • Revised Date :2022. 12. 27
  • Accepted Date : 2022. 12. 28