Anticipatory Notification System: Enhancing Disaster Response through Machine Learning and Email Alert

Abstract

The project "Anticipatory Notification System: Enhancing Disaster Response Through Machine Learning And Email Alert" addresses a web-based platform for predicting drought and flood natural disasters using machine learning with an email notification alert system targeted at disaster authorities. The purpose of the platform is to provide timely and accurate predictions of drought and flood events enabling proactive disaster management and response planning. The methodology involves collecting and preprocessing historical meteorological hydrological and geographical data. To train machine learning models various algorithms including decision trees random forests and neural networks are explored to identify the most effective approach for predicting drought and flood occurrences. The developed models are integrated into a user-friendly website interface allowing disaster authorities to access real-time predictions and historical trends results demonstrate the effectiveness of the machine learning models in forecasting drought and flood events with high accuracy and reliability the email notification alert system sends automated alerts to disaster authorities based on predefined thresholds ensuring timely dissemination of critical information for decision-making and emergency preparedness. In conclusion the developed platform provides a valuable tool for enhancing disaster prediction and management capabilities by leveraging machine learning techniques and real-time data integration. It empowers disaster authorities to take proactive measures to mitigate the impact of drought and flood disasters on vulnerable populations and infrastructure further enhancements such as incorporating additional data sources and refining prediction algorithms hold promise for improving the platforms accuracy and responsiveness in addressing the dynamic nature of natural disasters.