Omdena Spearheads Disaster Logistic Prediction Tool for Cyclones Commissioned by the World Food Programme
By Beth Seibel
Whether termed cyclone, typhoon or hurricane, these natural weather occurrences pack a serious punch and are responsible for approximately 10,000 deaths per year and, “in some cases, causing well over $100 billion in damage. There’s now evidence that the unnatural effects of human-caused global warming are already making hurricanes stronger and more destructive. The latest research shows the trend is likely to continue as long as the climate continues to warm (Berardelli, 2019).”
It is for these reasons that the World Food Programme teamed up with Omdena to more accurately predict the types and amount of aid required when disaster strikes. “Assisting almost 100 million people in around 83 countries each year, the World Food Programme (WFP) is the leading humanitarian organization saving lives and changing lives, delivering food assistance in emergencies and working with communities to improve nutrition and build resilience.”
Omdena gathered a team of 34 collaborators specializing in artificial intelligence and machine learning spanning 19 different countries for eight weeks with the goal of developing an AI data-driven way to help the WFP and other humanitarian organizations to know exactly what resources the people affected by cyclones (or any other disaster) will need and to expedite deployment as fast as possible. A priority on the team’s list, were answers to questions such as, how much food and water is required? What sort of shelters and how many are needed? What types and how much non-food essentials are appropriate? Before AI models could be developed, relevant data had to be gathered for this disaster response problem.
The team collected data from a variety of sources, such as NOAA, to determine affected populations and critical features of these populations such as income level, injuries, deaths, and more. Important factors were determined about cyclones including wind speed, total hours on land, damage factors, and whether the populations were rural versus urban. Below we see the correlation mapped based on income level and the number of people affected revealing populations most in need of assistance.
Understanding the attributes of the people affected by a disaster helps to reveal the types of aid required. So that the WFP and other aid organizations can determine what and how much relief to send with precision, the team used mathematical models to create a tool that calculates the needs of the people in the targeted disaster zones. The tool calculates how much food, non-food items, shelter, etc., the population should need for a determined number of days.
This exciting AI prototype can be used as the basis to assist disaster response organizations around the world to accurately customize aid resources to the specific needs of the people impacted. The team identified a more precise way to allocate aid in times of disaster. This will allow the World Food Programme and other organizations to respond to the needs of affected people faster and more efficiently than ever before thus reducing suffering and saving lives.
Find all details about the project here.
Berardelli, J. (2019, July 8). How climate change is making hurricanes more dangerous. Yale Climate Connections. Retrieved June 7, 2020, from https://www.yaleclimateconnections.org/2019/07/how-climate-change-is-making-hurricanes-more-dangerous/
World Food Programme Overview. (2020). Retrieved June 07, 2020, from https://www.wfp.org/overview