Disaster Prediction Models Using Integrated Data Systems

Abstract

Both natural and man-made calamities have major impacts to life, infrastructures, and economic stability across the globe. The rising rate and severity of catastrophes like earthquakes, floods, cyclones, landslides, wildfires and pandemics require the creation of sophisticated forecasting methods. Conventional ways of managing disasters tend to use isolated data sources and manual interpretation thereby providing restrictions on predictive accuracy and response frequency. The paper is a full predictive framework of disasters based on the integrated data system as a conglomeration of satellite imagery, Internet of Things (IoT) sensor data, meteorological data, geospatial databases, social media feeds and historic disaster data. The proposed model optimizes the situational awareness and early warning capabilities by using machine and deep learning. The research presents a multi-layered data acquisition, data preprocessing, and data trending, feature extraction, and predictive modeling. Sir-complicated algorithms that include the use of random forest, support vector machines, Convolutional Neural Networks, and Long Short-term Memory networks are used to process spatio-temporal patterns. The real-world datasets were used to test the system and show better prediction accuracy and lower response time. Findings from experimental outcomes show that holistic data-driven systems work far much better than traditional single source methodologies. The results point out the significance of data integration and smart analytics in disaster risk management. The study will help develop resilient smart disaster management systems and form a basis on the future advancements in real-time forecasting and emergency response planning.