Machine Learning Approach to Predict Flood in Bangladesh from Historical Data

Abstract

Floods are among the worst natural disasters, wreaking havoc on people's lives, property, infrastructure, and socioeconomic systems. Floods are a yearly nightmare in Bangladesh, causing varying degrees of damage from minor disruptions to total destruction. Bangladesh is a developing nation with a fragile economy, making it challenging to implement effective flood control measures for the major rivers that traverse the country. Numerous studies have focused on developing reliable flood forecasting systems and flood control. Still, it is difficult to predict the exact timing and volume of floods in real time. Accurate flood prediction requires the integration of computationally complicated flood propagation models with large amounts of data in order to detect water levels and velocities over large areas. By using machine learning algorithms to better accurately anticipate floods, this study aims to reduce the substantial risks associated with these events. The study will provide a more precise understanding of flood patterns by examining many factors, such as geographical location, year, monthly rainfall, and other hydrological and climatic variables. The research will make use of many machine learning models, including XGBoost, MLP Regressor, Gradient Boosting, Random Forest, Polynomial Regression, and K-Nearest Neighbors. We will carefully analyze each model's output to determine the optimal model for flood prediction. This study will not only forecast but also provide policy recommendations to help people be ready for and protect themselves against Bangladesh's devastating floods. We think that this comprehensive plan will help mitigate the effects of one of the hardest-hit natural catastrophes in the country.