Real-Time Flood Risk Assessment and Prediction Using Machine Learning and Remote Sensing Data

Abstract

Floods are among the most devastating disasters, presenting significant dangers to human life, property, and infrastructure. Accurate and prompt flood detection and forecasting are essential for disaster preparation, early warning systems, and risk management. Traditional methods often depend on both physical and statistical models, which may be constrained by data complexity and fluctuating environmental circumstances. A powerful substitute in recent years, “machine learning (ML)”, is skilled in spotting intricate, non-linear patterns in large datasets. This paper examines the use of many machine learning approaches, such as Decision Trees, Random Forests, “Support Vector Machines”, Neural Networks, “Gradient Boosting”, and Logistic Regression in flood prediction and detection. It emphasises the data sources, preprocessing techniques, and assessment measures, as well as integration with the “Internet of Things” (IoT) as well as edge computing technologies. The research also examines the strengths, limitations, and practical applicability of various models. The study continues by highlighting future research avenues, underscoring the promise of hybrid models, deep learning, as well as explainable AI in improving both the reliability and accuracy of flood prediction systems.