AI for Disaster Response: Improving Emergency Management

AI for Disaster Response: Improving Emergency Management

  • The Results
Project completed!

A collaborative Omdena team of 34 AI experts and data scientists worked with the World Food Programme to build solutions to predict affected populations and create customized relief packages for disaster response.

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

 

The problem: Quick disaster response

When a disaster strikes, the World Food Programme (WFP), as well as other humanitarian agencies, need to design comprehensive emergency operations. They need to know what to bring and in which quantity. How many shelters? How many tons of food? These needs assessments are conducted by humanitarian experts, based on the first information collected, their knowledge, and experience.

The project goal: Building a disaster relief package tool for cyclones (applicable to other use cases and disaster categories)

 

AI Disaster

Figure 1: Cyclone representation

 

Use Case: Cyclones (Solution applicable to other areas)

Tropical cyclones cost about 10,000 human lives a year. Many more are injured with homes and buildings destructed, which results in financial damage of several billions of USD. Due to changes in climate and extreme weather events, the impact is growing steadily.

The team mapped different correlation factors to determine which populations are most in need. As an example, below the income level is correlated with the number of people affected. Taking advantage of past data, the data model predicts affected populations.

 

AI Disaster Response

Figure 2: Predicting affected populations based on income level

The solutions: Calculating relief packages

Once an affected population is identified, humanitarian actors need to design comprehensive emergency operations including how much food and what type of food is needed. The project team built a food basket tool, which facilitates calculating the needs of affected populations. The tool looks for various different features such as days to be covered, the number of affected people, pregnancies, kids, etc.

 

AI Disaster Response

Figure 3: The relief package tool

 

The team: Collaborators from 19 countries

The WFP Innovation Accelerator hosted the project and worked with 34 collaborators and changemakers across four continents. All team members worked together for two months on Omdena´s innovation platform to build AI solutions with the mission to improve disaster response.

 

AI Disasters

Figure 4: Omdena Collaborators

 

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

 

This project has been hosted with our friends at