Disasters, which occur in periodic intervals, are natural phenomena such as
floods, landslides, etc. Nowadays, they are more frequent due to the effects of global
warming. These natural phenomena possess complex dynamics, making their precise
prediction a difficult task. The intention of smart cities is to use the advanced
technologies for flexible and sustainable solutions to such threats. In this paper, we
present a new and efficient approach for extending natural thread prediction using
mathematical fractal analysis to identify self-similar, multi-scale information that can
be incorporated into machine learning prediction models for natural disasters. In
addition, we used fractal generation techniques to create virtual replicas of the
metropolitan infrastructure in order to model reactions to extreme event forecasts. Our
multimodal method, at the nexus of urban modeling, machine learning, and fractal
mathematics, offers a groundbreaking way to improve climate preparedness for smart
cities. Adaptive resilience planning and early warning systems can benefit from the
application of data-driven infrastructure response techniques and catastrophe prediction
methodology.
Keywords: Convolutional neural networks, Climate resilience, Disaster preparedness, Early warning systems, Fractal analysis, Hurricane forecasting, Infrastructure planning, Long short-term memory.
