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Lillian Peterson

Los Alamos High School
Student
Lillian Petersen uses big data to investigate climate, agriculture, malnutrition, and poverty in developing countries. She specializes in remote monitoring as an early warning tool. In her talk, she will focus on her work to predict crop harvests in every country in Africa. Lillian is currently a student at Los Alamos High School and competes in science fairs at the national and international level.

Lillian Petersen's talk is titled: Predicting crop yields and malnutrition through integrated remote sensing and data assessment
Percentages of acute malnutrition continue to be unsettlingly high in developing countries, while coverage of treatment remains unsatisfactory. Here we present methods to predict the spatial prevalence of malnutrition throughout sub-saharan Africa. The training data can be broken into two sections: real-time and static variables. Real-time indicators include variables that could help analyze crop production and crop availability on the markets, such as crop yield predictions from daily MODIS satellite imagery, indicators of political stability (e.g. conflicts), news media reports, and food price indices. The satellite analysis methods have the ability to predict crop yields in every African country with a lead time of 2--4 months. This method measures relative vegetation health based on pixel-level monthly anomalies of NDVI, EVI and NDWI, and has been shown to produce correlations up to 0.99 in select African countries. Static variables include poverty indicators from the DHS, roads, urban agglomerations, night lights, major waterways, market potentials, and land cover. When all of these static and real-time predictors are joined using a machine learning algorithm, they are able to predict future malnutrition with reasonable accuracy. With a greater lead time, international aid organizations could more effectively distribute aid relief to those most in need.

My Speakers Sessions

Friday, October 18
 

8:30am GMT+05