Smart City Alert: Systems for Prevention and Detection of Disasters

Dear Colleagues,

The most vulnerable elements influencing both human life and the environment are disasters. Smart cities are mainly affected by disasters. This demonstrates the need for effective disaster management systems in urban areas.

Smart city alert systems integrate cutting-edge technologies to track a variety of variables, including weather conditions, environmental factors, and the condition of the infrastructure, in order to forecast and avoid possible disasters.

These novel platforms utilize machine learning and artificial intelligence (AI) to analyze prior data and predict disaster patterns.

With the ability to process vast amounts of data collected from sensors, satellites, and other sources, machine learning algorithms are increasingly becoming a powerful tool for smart city alert systems to identify potential vulnerabilities and predict failures in real time.

In addition, a smart city can provide city authorities with a comprehensive view of potential risks, enabling them to make informed decisions and take proactive measures to protect residents and infrastructure.

They represent a critical step in developing resilient, flexible, and sustainable urban environments, where technology plays a significant role in enhancing disaster preparedness and response, eventually ensuring the safety and well-being of urban populations.

For this Special Issue, we aim to present a collection of review and original research articles related to the latest technologies on the most recent developments in disaster detection and prevention systems, such as smart sensors, artificial intelligence-based damage identification, infrastructure disaster prediction, and early warnings for infrastructure safety.

Dr. Lorenzo Palma
Dr. Giovanni Paragliola
Guest Editors

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• smart cities
• smart sensors
• disaster prediction
• disaster prevention
• structural health monitoring
• sensor signal processing
• intelligent systems
• machine learning and deep learning