earticle

영어

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.