Real-Time Disaster Risk Assessment Using Predictive Geospatial Analytics

The increasing frequency and severity of natural disasters demand sophisticated approaches to risk assessment and emergency response. This study proposes a real-time disaster risk assessment framework using predictive geospatial analytics that integrates satellite imagery, environmental sensors, and socio-economic data. Leveraging machine learning models such as Random Forest, Gradient Boosting, and Spatio-temporal LSTM networks, this research evaluates disaster risk across spatial and temporal dimensions. Predictive analytics are applied to historical disaster datasets including floods, wildfires, and earthquakes, while real-time environmental sensor data and weather feeds enable adaptive risk forecasting. Statistical analysis and regression models reveal the influence of variables such as rainfall, temperature anomalies, population density, and vegetation indices. The models demonstrate high performance in forecasting risk zones with an AUC-ROC above 0.90. The study emphasizes the potential of real-time geospatial analytics for proactive disaster mitigation and calls for integration into national disaster management strategies. Keywords: Disaster Risk Assessment, Predictive Analytics, Geospatial Data, Real-Time Forecasting, Machine Learning, Satellite Imagery