Natural Hazards Research Summit 2024: Enhancing Urban Seismic Resilience: Integrating AI and Monte Carlo Methods for Real-Time Hazard Prediction

This poster presents an innovative approach to enhance the seismic resilience of urban water distribution systems by integrating Artificial Intelligence (AI) and Monte Carlo (MC) simulations. The primary goal is to develop an AI-driven framework that can process real-time seismic data to rapidly assess potential impacts on water distribution infrastructure. This framework utilizes machine learning models, trained on extensive datasets of seismic activity, geological information, and detailed characteristics of water systems, to predict the effects of seismic events as they unfold.
A distinctive feature of this research is the application of Monte Carlo methods to introduce a probabilistic dimension to the predictions, quantifying uncertainties inherent in real-time seismic analysis. This feature is crucial in understanding the range of potential impacts on water distribution, thereby aiding emergency response planning with more nuanced, data-driven insights. The proposed research seeks to make a significant contribution to the field of seismic hazard analysis and emergency preparedness for urban water systems. By combining advanced AI modeling with probabilistic simulation techniques, the goal is to create a tool that enhances the capacity of urban centers to maintain water distribution services more effectively during seismic events, ultimately leading to more resilient and better-prepared urban environments.