Predicting Future Change of Seasonal Rainfall and Temperature in Bangaldesh Using Machine Learning Models

Abstract

Understanding the dynamic shifts in rainfall and temperature holds immense significance for countries like Bangladesh grappling with critical climate conditions. Natural disasters such as floods, droughts, and cyclones are intricately tied to variations in these climatic factors. Therefore, the ability to predict future patterns of rainfall and temperature is crucial for effective disaster management. While numerous methods exist for forecasting these conditions, the exploration of predicting future changes in seasonal rainfall and temperature using diverse machine learning models and assessing their accuracy remains unexplored. This thesis aims to fill this gap by systematically comparing six machine learning models such as Linear Regression, Polynomial Regression, Decision Tree, Random Forest, Adaboost and Gradient Boosting Machines to forecast the seasonal variations in rainfall and temperature for Bangladesh. The emphasis is on identifying the most accurate model through rigorous comparisons. The selected optimal machine learning model is subsequently employed to project seasonal rainfall and temperature conditions for the year 2025 to 2074. The analysis extends further to examine the percentage change in seasonal rainfall and change in average temperature between the years 2024 and 2050. By adopting this approach, the research contributes to advancing the understanding of climate prediction methodologies, particularly in the context of a country facing acute climate challenges like Bangladesh.