Helping affected populations during a disaster most effectively through AI technology. 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 AI Disaster prevention.
The entire data analysis and details about the relief package tool including a live demonstration can be found in the demo day recording at the end of the article.
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)
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 Omdena team gathered data from several sources:
- IBTrACS – Tropical cyclone data that provides climatological speed and directions of storms (National Oceanic and Atmospheric Administration)
- EmDAT – Geographical, temporal, human, and economic information on disasters at the country level. (Université Catholique de Louvain)
- Socio-Economic Factors from World Bank
- The Gridded Population of the World (GPW) collection – Models the distribution of the human population (counts and densities) on a continuous global raster surface
Missing data was collected manually or partially automated by scraping from Wikipedia or cyclone reports.
Data exploration: Determining affected populations
All five data set were aggregated and included more than 1000 events and 45 features characterizing cyclones and affected populations.
Important correlation factors to determine affected populations:
- Rural Population
- Human Development Index
- GDP per capita
- Wind Speed
- Exposed population
- Total hours in Land
- Total damage
- Total deaths
The team mapped the 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.
The tool: Calculating relief packages
Once an affected population has been 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.
The entire data analysis and details about the relief package tool including a live demonstration can be found in the video.
The team: Collaborators from 19 countries
This Omdena project hosted by the WFP Innovation Accelerator united 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. To learn more about the project check out our project page.
All changemakers: Ali El-Kassas, Alolaywi Ahmed Sami, Anel Nurkayeva, Arnab Saha, Beata Baczynska, Begoña Echavarren Sánchez, Chinmay Krishnan, Dev Bharti, Devika Bhatia, Erick Almaraz, Fabiana Castiblanco, Francis Onyango, Geethanjali Battula, Grivine Ochieng, Jeremiah Kamama, Joseph Itopa Abubakar, Juber Rahman, Krysztof Ausgustowski, Madhurya Shivaram, Onassis Nottage, Pratibha Gupta, Raghuram Nandepu, Rishab Balakrishnan, Rohit Nagotkar, Rosana de Oliveira Gomes, Sagar Devkate, Sijuade Oguntayyo, Susanne Brockmann, Tefy Lucky Rakotomahefa, Tiago Cunha Montenegro, Vamsi Krishna Gutta, Xavier Torres, Yousof Mardoukhi
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