Dynamic Climate Hazard Prediction: A Real-time Web Application

The model for developing the predictive climate resilience and disaster management in the most vulnerable regions is discussed in the current paper. At its base, the model enhances climate hazard forecasting by real-time data gathered through API. Training on historic and live meteorological and environmental data, the model auto-molds itself to keep pace with the evolving climate pattern and makes reliable prediction. Besides, for distributing warnings and notifications in timely fashion, a user-oriented application based on Flask is developed. This might be a comprehensive system of information at the community level that aids in proactive preparedness with effective response strategies. The proposed model and application enhance big goals of climate resilience and disaster mitigation by proving accurate and real-time forecasting, easily accessible communication regarding potential risks.