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IEEE Climate Change Presents: AI-Driven Solutions for Inland & Coastal Flooding
Artificial Intelligence (AI) has the potential to play a significant role in disaster management, including mitigating the impact of extreme weather events, such as Cyclone Gabrielle in NZ. While AI cannot directly prevent or stop a cyclone from occurring, it can be utilized in several ways to reduce its impact and improve disaster preparedness and response.
When Cyclone Gabrielle hit New Zealand on the 12th of February 2023, the country was not ready. Not because the weather forecasters did not see it coming but because of the weight of successive storms with intense rainfall that happened across the North Island for the previous month and a half. The quantity of rainfall was unprecedented. The Northern part of the North Island experienced rainfall that was at least 400% above the normal levels for January.
Our study aims to demonstrate and develop efficient tools using new technology combining spatial analysis and artificial intelligence. Our most significant development is in predictive analytics. AI algorithms can analyze historical data and real-time information to generate predictive models that forecast a crisis’s potential impact and trajectory. Our AI-based risk assessment models can analyze multiple factors, including geographical data, infrastructure vulnerabilities, demographic information, and historical records, to identify higher-risk areas and prioritize emergency efforts.
The successive weather events in NZ earlier this year have inflicted substantial damage on the North Island, resulting in significant financial costs. It is crucial to recognize that as weather patterns continue to change, communities must adapt and enhance their preparedness strategies to respond effectively to unprecedented weather events. One way to help our communities adapt is by improving our capabilities in early warning systems and predictive analytics.
Moderator/facilitator: Dr Atiya, Dr. Chen or Sir Fahad Najeeb
Meet Dr. Phil Mourot, a senior data scientist with over 25 years of experience in natural hazards and early warning system systems. With a Ph.D. in geophysics from France, Phil specializes in developing new methods and tools to predict natural disasters. He has extensive field experience, from analyzing the Mont Blanc glaciers in the French Alps to monitoring the Merapi volcano in Indonesia. In New Zealand since 2015, Phil is now a Senior Hazard Advisor for the Waikato Regional Council and advocates resilience to reduce disaster risk and support climate adaptation. Two years ago, Phil joined the TAIAO team from the University of Waikato, and his research focuses on predicting the impact of floods using deep learning and improving emergency management during a crisis.