Quantifying Climate Change Effects on Standard Minimum and Maximum Average Temperature Extremes in Bangladesh: A Machine Learning Regression Analysis from Past to Present

The impact of climate change on temperature extremes is particularly significant in regions like Bangladesh, and it is a pressing global concern. To quantify the effects of climate change on Bangladesh’s standard minimum and maximum average temperature extremes, this study uses machine learning techniques on historical records from 1981 to 2010 with current data from 2022. By utilising information from the Statistical Yearbook Bangladesh 2022, which includes temperature readings from 44 different stations, this study offers a thorough evaluation of temperature fluctuations in various geographic areas. Several machine learning models, including Random Forest, Ridge Regression, Bayesian Ridge Regression, K-Nearest Neighbours Regression, and an Ensemble Model, are applied as part of the methodology. The following evaluation metrics are used: R-squared (R²), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Root Mean Squared Logarithmic Error (RMSLE). The study’s conclusions highlight notable yearly temperature variations and dynamic patterns of climate change in important places like Khulna, Chattogram, Teknaf, and Dhaka. The outcomes show that, compared to other models, the Ensemble Model performs better across evaluation metrics and is the most successful at predicting temperature extremes. Furthermore, comparing the actual and predicted temperatures for a few selected stations reveals significant deviations that underscore Bangladesh’s changing climate dynamics. This research enhances our understanding of the impact of climate change on temperature extremes in Bangladesh. This study helps stakeholders, researchers, and policymakers better understand the difficulties brought on by the region’s changing climate patterns.