Abstract

This study explores the complex factors that affect the intensity of monsoons and their impact on the environment and society. The main objective is to provide practical insights for effective management of monsoons and strategies to reduce risks. This study examines the complex influence of various factors on monsoon intensity using a rigorous methodology that includes extensive data collection, meticulous pre-processing, insightful exploratory data analysis, and sophisticated modeling techniques such as Random Forest, Decision Tree, and Linear Regression. The results highlight the superiority of Linear Regression as the most effective model, as evidenced by its minimal Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values, as well as its exceptional fit indicated by the high R-squared (R2) score. The findings highlight the ability of the model to effectively capture the complex interactions present in the dataset. However, it is important to carefully consider the specific contextual nuances and requirements of the problem when choosing the appropriate model. This study provides valuable insights that can be used to develop strong strategies for dealing with the challenges of monsoon variability and its related risks. This will help promote resilience in both the environment and society.