DIGITAL TWIN AND IOT SENSOR–ENABLED FRAMEWORK FOR REAL-TIME COMPLEX DISASTER MONITORING, INTELLIGENT EARLY WARNING, EMERGENCY RESPONSE AND CRISIS MANAGEMENT

Abstract

Rapidly evolving disasters demand monitoring systems capable of integrating heterogeneous data, forecasting cascading hazards, and supporting timely emergency decisions. This study proposes a Digital Twin and Internet of Things (IoT) sensor–enabled framework for real-time complex disaster monitoring, intelligent early warning, emergency response, and crisis management. A multimodal dataset containing 1,250,000 time-stamped observations was constructed from 420 IoT devices deployed across five disaster-prone zones over 24 months. The devices included seismic sensors, river-level gauges, weather stations, smoke and gas detectors, structural-vibration sensors, surveillance nodes, GPS units, and wearable health monitors. The dataset covered earthquakes, floods, landslides, urban fires, and industrial gas-leak events, with 52 environmental, structural, spatial, physiological, and operational variables. After missing-value treatment, noise removal, temporal synchronization, normalization, feature selection, and class balancing, records were divided into training, validation, and testing sets using a leakage-free 70:15:15 strategy. The proposed architecture continuously synchronized physical conditions with a dynamic Digital Twin, while a hybrid convolutional neural network–bidirectional long short-term memory–attention model extracted spatial features, learned temporal dependencies, and prioritized hazard indicators. Geographic information systems, edge computing, and cloud analytics supported risk visualization, low-latency inference, evacuation-route optimization, resource allocation, and coordinated emergency communication. Experimental results showed that the framework achieved 96.8% disaster-event classification accuracy, 96.1% precision, 95.7% recall, a 95.9% F1-score, and an area under the receiver operating characteristic curve of 0.984. It reduced false alarms by 31.6%, improved warning lead time by 27.4%, decreased average detection latency from 8.7 to 2.1 seconds, and increased emergency-resource allocation efficiency by 22.8% compared with conventional monitoring approaches. Digital Twin–based scenario simulation further improved evacuation-route selection by 19.5% and reduced estimated emergency response time by 24.2%. It also maintained robust performance during sensor failures, communication disruptions, and changing hazard conditions through adaptive data fusion mechanisms. These findings demonstrate that integrating Digital Twins, distributed IoT sensing, and explainable artificial intelligence can provide accurate situational awareness and actionable decision support across interconnected disaster stages. The framework offers a scalable foundation for resilient cities, emergency agencies, and critical-infrastructure operators, although broader field validation and privacy-preserving data governance remain necessary.