A hierarchical Bayesian regression framework to analyze climate data from Central Asia region | ||
| The Egyptian Journal of Environmental Change | ||
| Article 3, Volume 16, Issue 2, October 2024, Pages 31-50 PDF (2.62 M) | ||
| Document Type: Peer-reviewed articles | ||
| DOI: 10.21608/ejec.2024.381182 | ||
| Abstract | ||
| This study introduces a straightforward framework for analyzing climate data related to the minimum and maximum temperatures of countries in Central Asia (Kazakhstan, Kyrgyzstan, Tadjikistan, Turkmenistan, and Uzbekistan), considering annual temperature averages over a long period of time ranging from the early 1900's to the beginning of the year 2000. The data analysis used standard existing multiple linear regression models under a hierarchical Bayesian approach, assuming as covariates latitude and longitude of the climate stations, temporal factors (linear, quadratic, and cubic effects of years), and altitude of the climate station. The findings yielded highly accurate results in identifying significant factors influencing climate change, such as time (year), altitude, and spatial factors, as well as in predicting average temperatures in future years. Furthermore, the obtained results align with numerous other studies in the literature, indicating that all regions of the world are already experiencing climate change. In particular, we observed that annual average minimum temperatures in Central Asia are increasing in the five countries assumed in the study at the end of the follow-up period (close to the year 2003). We also observed similar results for the annual average maximum temperatures. | ||
| Keywords | ||
| climate data; multiple linear regression models; Bayesian inference; MCMC métodos; minimum and maximum yearly average temperatures | ||
| References | ||
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