Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning

Abstract

Over recent years, the frequency and intensity of fire disasters have escalated, underscoring the urgent need for accurate and timely disaster classification and mitigation strategies. The proposed approach integrates fine-tuned deep learning models with real-time sensor data and digital twin technology to enhance fire disaster detection and early mitigation capabilities. This methodology leverages diverse data sources, including real-time Internet of Things (IoT) sensor readings, satellite imagery, and historical fire incident data. Advanced deep learning architectures such as convolutional neural networks (CNNs), deep CNNs (DCNNs), and recurrent neural networks (RNNs) are utilized to identify critical spatial and temporal patterns in the data. The models are trained on a comprehensive dataset encompassing environmental indicators, fire-prone area characteristics, and real-time meteorological data. A key innovation is the use of digital twin technology, which dynamically integrates real-time data from IoT sensors and simulation models to predict fire disaster scenarios accurately. The integration of digital twins enables continuous monitoring and virtual replication of physical systems, facilitating proactive decision-making and early warnings. This real-time assimilation enables proactive decision-making and resource allocation tailored to diverse geographical and socio-economic contexts. Performance evaluations reveal that the DCNN+Digital twin framework achieves a 99% classification accuracy with a reduced error rate of 3% over 500 runs, outperforming standalone models, such as RNN (90% accuracy, 10% error), CNN (96% accuracy, 8% error), and DCNN (97% accuracy, 6% error). The proposed approach offers a transformative solution to fire disaster management, promoting community resilience, environmental sustainability, and effective mitigation strategies in the face of escalating fire risks.

Topics & Concepts