Robust Data-Driven System for cloud Burst using Machine Learning Techniques

Flash floods are caused by cloud bursts. In many parts of the world, they represent a serious threat to both human life and infrastructure. Timely and accurate prediction of these extreme weather events is crucial for minimizing their devastating impact. The proposed system aims to develop a cloud burst prediction model tailored to a specific geographic region. The primary objective is to create a robust, data-driven system that leverages historical weather data, topographical information, and advanced machine learning techniques to provide early warnings and forecasts for cloud bursts. The proposed system involves comprehensive data collection, pre-processing, and feature engineering to make the data suitable for analysis. Various machine learning and statistical models like Random forest, support vector machine, logistic regression, A user-friendly interface is developed to make the predictions accessible to meteorological experts and the general public. Collaboration with local authorities and emergency response agencies will ensure that the predictions can be effectively integrated into decision-making processes and public safety measures.