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2022 Vol.15, Issue 4 Preview Page
31 December 2022. pp. 87-98
Abstract
References
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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
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Hong, W. C. (2008). Rainfall Forecasting by Technological Machine Learning Models. AMC, 200(1): 41-57. 10.1016/j.amc.2007.10.046
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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
4
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
5
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
6
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
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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
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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
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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
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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

1
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
2
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
3
이석호, 강동호, 김병식 (2018). 호우영향예보를 위한 한계강우량 산정 방법 연구. 한국방재학회논문집. 18(7): 93-102.
4
이한수, 지용근, 이영미, 김병식 (2021). 호우 영향예보를 위한 머신러닝 기반의 수문학적 정량강우예측(HQPF) 최적화 방안. 한국환경과학회지. 30(12): 891-905.
Information
  • 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