ADVANCED STATISTICAL MODELS FOR PREDICTING TEMPERATURE EXTREMES IN BANGKOK

Abstract

In a world where climate change is accelerating due to human activities, understanding and predicting temperature variations have become critical. This study focuses on Bangkok, the bustling capital of Thailand, where economic activities and population density are high. Fluctuations in temperature are influenced by various factors, including greenhouse gas emissions, solar radiation, geographical location, and local conditions like ground and water surfaces. To address the challenge of temperature variability, this research employs simple time series analysis to develop statistical models for forecasting daily maximum and minimum temperatures in Bangkok. These models serve a vital purpose by helping residents, businesses, and policymakers prepare for and mitigate the impacts of meteorological phenomena. As global temperatures continue to rise, such predictive tools are invaluable for adapting to the changing climate and minimizing potential losses.