Artificial Intelligence and Machine Learning for Predicting Natural Disasters in Smart City Infrastructure

As smart cities emerge worldwide, the integration of real-time data in multiple sectors, including transportation, energy, and public services, is starting to surface. Among the unexplored frontiers within this data landscape, vital for fortifying resilient and efficient urban services, lies the realm of natural disaster prediction and mitigation. This chapter aims to address the first phase in developing intelligent, datadriven tools for natural disaster prediction in smart city infrastructure. The integration of Artificial Intelligence (AI) and Machine Learning (ML) models within smart city infrastructure stands as a pivotal advancement, offering an array of benefits in disaster prediction and management. Techniques such as neural networks, decision trees, random forests, and support vector machines have proven instrumental in this regard. This chapter is of high relevance to the field currently due to the increasing sophistication and range of AI technologies, as well as the increasing capacity for realtime data collection facilitated by smart city technology. The focus on combining AI approaches with the unique challenges and opportunities presented by smart city infrastructure makes this a timely and essential project for the field and an excellent driver for knowledge exchange with both the academic community and industry in both AI and smart city development. Despite facing obstacles, such as legal and social concerns, the ongoing progress and widespread acceptance of AI-based solutions offer significant potential for strengthening disaster preparedness and improving the overall quality of life in cities