Background and Rationale
The Caribbean is subject to a constant cycle of hurricanes, which dictate the rhythm of life on the islands. These seasonal storms pose significant threats, including catastrophic winds, storm surges, and prolonged flooding, which have profound impacts on the lives of the people living in the Caribbean.
Africa is directly conn
The Caribbean is subject to a constant cycle of hurricanes, which dictate the rhythm of life on the islands. These seasonal storms pose significant threats, including catastrophic winds, storm surges, and prolonged flooding, which have profound impacts on the lives of the people living in the Caribbean.
Africa is directly conn
ected to Caribbean hurricanes through a weather pattern known as "African easterly waves." Approximately half of all Atlantic hurricanes—and most of the long-lived, major ones—originate as clusters of thunderstorms over the African continent before traveling thousands of miles across the Atlantic Ocean and intensifying. Given these interconnections on climate systems, predicting and managing hurricanes necessitates the collaborations of Caribbean and West Africa. Simply managing the effects of hurricanes within the Caribbean is not a viable long-term solution. While this panel will primarily focus on AI-based prediction of disasters due to climate change, the outcomes of the discussions can be extended to disasters caused by earthquakes, fires, and extreme natural events.
Traditionally, climate prediction techniques have relied on intricate numerical models based on simulations executed on powerful supercomputers. New AI approaches utilizing machine learning algorithms to analyze extensive historical data are increasingly promising. These AI-driven methods not only enhance climate prediction but also transform disaster management by transitioning from reactive responses ad-hoc approaches to proactive hazard prediction and expedited intelligent resource allocation. AI systems seamlessly integrate real-time sensor, drone, and satellite data to map risks, optimize emergency logistics, and coordinate relief efforts.
Proposed Panel Discussion Topics
The panel discussion will be centered around, but not limited to, the following topics:
Expected Outcomes:
Call for Panelists
The panel organizers are seeking panelists for the panel discussions. If you're interested in participating as a panelist, please contact the panel organizers.
Dr. David White, UTech
Traditionally, climate prediction techniques have relied on intricate numerical models based on simulations executed on powerful supercomputers. New AI approaches utilizing machine learning algorithms to analyze extensive historical data are increasingly promising. These AI-driven methods not only enhance climate prediction but also transform disaster management by transitioning from reactive responses ad-hoc approaches to proactive hazard prediction and expedited intelligent resource allocation. AI systems seamlessly integrate real-time sensor, drone, and satellite data to map risks, optimize emergency logistics, and coordinate relief efforts.
Proposed Panel Discussion Topics
The panel discussion will be centered around, but not limited to, the following topics:
- AI-informed Climate Modes: Integrating numerical partial differential equation computations with Physics-informed machine learning (neural networks) to reduce the computational complexity and energy requirement of complex climate mode
- Intelligent Climate Disaster Management Systems: Automated Early Warning Systems in smartphones and edge devices. AI-enabled search and rescue drones, resource allocation systems.
- Africa/Caribbean Climate Collaboration: Exploration of collaborative climate research activities between the Caribbean and Africa, particularly in the collection and sharing of data, and North America. This report explores climate research activities between the Caribbean and Africa. This may include the deployment of sensors in the Sahel regions of Africa to collect data on the West African Monsoon and its associated Sahel rainfall, which impact the severity of Caribbean hurricanes.
- Regional Climate Data Center: Data sharing and interoperable warning systems. Joint research and educational initiatives. Building regional capacity in AI-driven disaster management.
Expected Outcomes:
- ow the Caribbean region can collaborate to leverage AI for improved climate and earthquake disaster prediction and management, such as intelligent early warning systems and fair resource allocations post-disaster, without relying heavily on external help.
- Develop strategies for using AI to enhance climate and earthquake research investment in Caribbean universities, given the region's critical industries like tourism, hospitality, logistics, maritime trade, and mining.
- Explore collaborative climate research activities between the Caribbean, Africa, and North America, including data collection and sharing, and deploying sensors in the Sahel regions of Africa to gather data on the West African Monsoon and its impact on Caribbean hurricanes.
Call for Panelists
The panel organizers are seeking panelists for the panel discussions. If you're interested in participating as a panelist, please contact the panel organizers.
Dr. David White, UTech
-