Flood Detection on Adverse Natural Conditions using CatBoost algorithm

This study shows a good way to find floods when the weather is bad by using the CatBoost algorithm. It is very important to have accurate and quick tools for finding floods so that people, property, and the environment are safe from this huge danger. The CatBoost algorithm is used in this work to improve the accuracy of flood warning models. This algorithm is known for its ability to handle categorical data and make good estimates. The suggested way looks at places that are likely to flood by using source saturation pressure, inside width, mass flux, heat flux, and refrigerant properties. Our results, which came from a lot of testing and confirmation, show that the CatBoost algorithm can correctly find and predict floods, even when the environment is very bad. This work improves flood monitoring technology and offers a viable weather early warning tool.