Use of Predictive Analytics in Humanitarian Operations
Abstract
As humanitarian crises around the world intensify, there is a growing urge to find innovative solutions for humanitarian aid. One emerging solution is to use predictive analytics, a type of artificial intelligence, to predict the timing, scale, and scope of humanitarian crises. Ranging from predicting the levels of severe flooding to the duration and intensity of a conflict-related displacement crisis, humanitarian use of predictive analytics can allow humanitarian actors to respond to impending crises before they occur, saving lives, time, and money. The use of predictive analytics in the humanitarian sector is fairly new, emerging within the last 10 years and ramping up significantly by the end of the 2010’s. However, as these projects and initiatives are all fairly new, there is not extensive research on the use of predictive analytics in humanitarian operations. This is important to the field of humanitarian studies because as humanitarian crises become more frequent, harsh, and complex, new solutions are needed to efficiently and effectively provide humanitarian aid. Anticipatory action, humanitarian projects that utilize predictive analytics, focus on bridging the gap between humanitarian and development work to anticipate community needs before a crisis reaches its crest. Pre-positioning humanitarian aid can save lives, livelihoods, infrastructure, and money. Additionally, preparing in advance of quick and slow onset disasters can help protect years of development efforts, limiting the need for families to “start over” after each crisis. Using case studies derived from academic sources and humanitarian operational reports, this thesis focuses on the use of predictive analytics technology in humanitarian operations, concentrating mainly on the types of data used to feed the predictive analytics algorithms; examples of humanitarian programs using predictive analytics; and the benefits, challenges, and risks of using this emerging technology. Synthesizing data from humanitarian operational reports, programmatic toolkits, and academic sources, this thesis aims to contribute to the body of literature on this subject by providing a critical lens on the intersection of humanitarian action and predictive analytics technology.
Subject Area
International Relations|Computer science
Recommended Citation
Jelonek, Aleksandra Christine, "Use of Predictive Analytics in Humanitarian Operations" (2023). ETD Collection for Fordham University. AAI30246999.
https://research.library.fordham.edu/dissertations/AAI30246999
