An AI-Driven Approach to Improving Disaster Emergency Response

Project completed!

Background

Natural disasters such as tropical cyclones have devastating effects, claiming approximately 10,000 lives annually and causing billions in financial losses. Humanitarian agencies, including the World Food Programme (WFP), must rapidly assess needs to deliver appropriate relief. However, traditional methods based on experience and limited data often struggle to address the growing challenges of climate change and extreme weather events. AI offers a transformative approach to enhancing disaster response efficiency and precision.

Figure 1: Tropical Cyclone Amphan infographic

Objective

The primary goal of this project was to build an AI-powered disaster relief package tool, starting with cyclone-specific use cases and adaptable to other disaster scenarios. The solution aimed to predict affected populations and design comprehensive relief operations, ensuring optimal resource allocation tailored to community needs.

Approach

To tackle this challenge, the team employed the following main steps:

Data Analysis: Mapped correlation factors such as income levels and past disaster data to identify populations most at risk.
AI Modeling: Developed predictive models to estimate affected populations using historical cyclone data.

Figure 2: Cyclone Pabuk route (Affected countries)

Relief Package Tool: Created a food basket tool to calculate relief needs based on variables like days of coverage, number of affected people, and demographic details (e.g., pregnancies, children).

Figure 3: Relief package tool

Visualization: Demonstrated results and functionalities through a live demo during the project’s conclusion.

The project utilized diverse datasets and state-of-the-art machine learning techniques to achieve actionable insights.

Results and Impact

The AI-driven solutions developed during the project have significantly improved disaster response capabilities:

Population Prediction: The AI model accurately predicts the number of individuals affected by cyclones, enabling proactive planning.
Custom Relief Packages: The food basket tool provides precise calculations for essential supplies, ensuring the right resources reach the right people.
Broad Application: Although initially designed for cyclones, the tools can be adapted to various disaster scenarios.

The collaboration has empowered humanitarian actors to design data-driven emergency operations, reducing response times and enhancing the impact of relief efforts.

The complete data analysis and details about the relief package tool, including a live demonstration, can be found in the demo day recording below.

Future Implications

The findings from this project demonstrate the potential of AI in reshaping disaster response strategies. By integrating predictive models into standard operational workflows, humanitarian agencies can better prepare for and mitigate the effects of future disasters. This work also lays the foundation for further research into expanding AI applications for disaster management, including real-time response optimization and resource allocation across diverse disaster types.