Flood Risk Assesment System using Logistic Regression

One of the most frequent natural disasters in the world, flooding affects millions of people annually and causes significant economic losses as well as human tragedies, with India bearing a disproportionate amount of the burden. Reducing these effects requires effective early warning systems, yet many vulnerable communities still struggle to make use of them. Scientists and programmers are using advances in machine learning (ML) and artificial intelligence (AI) to bridge this gap by creating a trustworthy flood prediction system. This state-of-the-art system aims to offer more cost-effective choices that perform better at predicting floods caused by precipitation. This prediction system’s primary job is to forecast potential flood events by utilizing previous rainfall data to create predictive models. The model looks at rainfall patterns specific to different locations to determine the likelihood of a flood event. Its growth depends on the utilization of extensive district-level rainfall information from India, which serve as the foundation for training and enhancing the prediction models. A range of machine learning (ML) techniques, such as XGBoost and K-Nearest Neighbors (KNN), are used to train predictive algorithms. These algorithms were chosen because of their ability to handle complex datasets and extract meaningful patterns that relate the amount of rainfall with the probability of floods. Through recurrent training and validation against past flood events, the models are improved to reliably determine whether a region is in danger of flooding based on measured rainfall amounts.