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Analysis of Disaster Occurrences in Mongolia Based on Climatic Variables

기후변수를 기반으로 한 몽골 재해발생 분석

  • Da Hye Lee (Department of Computer Science and Statistic, Chosun University) ;
  • Onon-Ujin Otgonbayar (Department of Computer Science and Statistic, Chosun University) ;
  • In Hong Chang (Department of Computer Science and Statistic, Chosun University)
  • 이다혜 (조선대학교 컴퓨터통계학과) ;
  • 오트공바야르 우진 (조선대학교 전산통계학과) ;
  • 장인홍 (조선대학교 컴퓨터통계학과)
  • Received : 2024.08.27
  • Accepted : 2024.09.04
  • Published : 2024.09.30

Abstract

Mongolia's diverse geographical landscape and harsh climate make it particularly susceptible to various natural disasters, including forest fires, heavy rains, dust storms, and heavy snow. This study aims to explore the relationships between key climatic variables and the frequency of these disasters. We collected monthly data from January 2022 to April 2024, encompassing average temperature, temperature variability (absolute temperature difference), average humidity, and precipitation across the capitals of Mongolia's 21 provinces and the capital city Ulaanbaatar. The data were analyzed using multiple statistical models: Linear Regression, Poisson Regression, and Negative Binomial Regression. Descriptive statistics provided initial insights into the variability and distribution of the climatic variables and disaster occurrences. The models aimed to identify significant predictors and quantify their impact on disaster frequencies. Our approach involved standardizing the predictor variables to ensure comparability and interpretability of the regression coefficients. Our findings indicate that climatic variables significantly affect the frequency of natural disasters. The Negative Binomial Regression model was particularly suitable for our data, which exhibited overdispersion common characteristic in count data such as disaster occurrences. Understanding these relationships is crucial for developing targeted disaster management strategies and policies to mitigate the adverse effects of climate change on Mongolian communities. This research provides valuable insights into how climatic changes impact disaster occurrences, offering a foundation for informed decision-making and policy development to enhance community resilience.

Keywords

Acknowledgement

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2019S1A6A3A01059888).

References

  1. Ozaki, K. and Takakura, K., "Mongolia's unique climate and its impacts on society.", Mongolian Climate Research, Vol. 29, No. 3, pp. 145-162, 2021.
  2. Myagmarsuren, D. and Batkhuyag, B., "Climate change impacts on pastoralism in Mongolia.", Environmental Research Letters, Vol. 16, No. 5, p. 054034, 2021.
  3. Han, J., Zhou, W. and Fischer, T., "Desertification in Mongolia: A new climate regime.", Climate Dynamics, Vol. 56, pp. 3435-3450, 2021.
  4. Chadraabal, B., Byambasuren, O. and Khorloo, B., "The adaptive capacity of Mongolian pastoralists to climate change.", Journal of Environmental Management, Vol. 256, p. 109835, 2020.
  5. Flannigan, M. D., Krawchuk, M. A., de Groot, W. J., Wotton, B. M. and Gowman, L. M., "Implications of changing climate for global wildland fire.", International Journal of Wildland Fire, Vol. 18, No. 5, pp. 483-507, 2009.
  6. Westerling, A. L., Hidalgo, H. G., Cayan, D. R. and Swetnam, T. W., "Warming and earlier spring increase western U.S. forest wildfire activity.", Science, Vol. 313, No. 5789, pp. 940-943, 2006
  7. Trenberth, K. E., "Changes in precipitation with climate change.", Climate Research, Vol. 47, No. 1-2, pp. 123-138, 2011.
  8. Allan, R. P. and Soden, B. J., "Atmospheric warming and the amplification of precipitation extremes.", Science, Vol. 321, No. 5895, pp. 1481-1484, 2008.
  9. O'Gorman, P. A., "Contrasting responses of mean and extreme snowfall to climate change.", Nature, Vol. 512, No. 7515, pp. 416-418, 2014.
  10. Shao, Y. and Dong, C. H., "A review on East Asian dust storm climate, modelling and monitoring.", Global and Planetary Change, Vol. 52, No. 1-4, pp. 1-22, 2006.
  11. Preisler, H. K., Brillinger, D. R., Burgan, R. E. and Benoit, J. W., "Probability-based models for estimating wildfire risk.", International Journal of Wildland Fire, Vol. 13, No. 2, pp. 133-142, 2004.
  12. Guhathakurta, P. and Saji, E., "Detecting changes in rainfall pattern and seasonality index vis-a-vis increasing water scarcity in Maharashtra.", Journal of Earth System Science, Vol. 118, No. 4, pp. 273-284, 2009.
  13. Liu, Z., Yang, J., He, H. S. and He, R., "Predicting wildfire occurrence in the Missouri Ozarks using a random forest classifier.", Forest Ecology and Management, Vol. 356, pp. 12-19, 2015.
  14. Krizhevsky, A., Sutskever, I. and Hinton, G. E., "ImageNet classification with deep convolutional neural networks.", in Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
  15. Kim, Y. S., Song, K. Y., and Chang, I. H., "Classification Abnormal temperatures based on Meteorological Environment using Random forests", J. Integrative Natural Sci., Vol. 17, No. 1, pp. 1-12, 2024.
  16. Na, J. H., Applied Regression Analysis, FREEACADEMY, 2017.