PhD Defense Seminar - By PhD Candidate - Chrisgone Adede

Model Ensembles for Predictive Drought Severity and Drought Effects Monitoring using Remote Sensing & Socio-Economic data

Droughts, with their increasing frequency of occurrence, especially in the Greater Horn of Africa (GHA), continue to negatively affect lives and livelihoods. For example, the 2011 drought in East Africa caused massive losses documented to have cost the Kenyan economy over $12bn.Consequently, the demand is ever-increasing for ex-ante drought early warning systems with not only the ability to offer drought forecasts with sufficient lead times but that are both stable and are of high bias. In this study, we build predictive models one month ahead for both drought severity and drought effects. Vegetation condition index aggregated over 3 months (VCI3M) and nutrition of children below 5 years as indicated by middle upper arm circumference (MUAC) are used as the proxy variables for drought severity and drought effects respectively. We present the performance of both homogenous and heterogenous model ensembles in the prediction of drought severity and droughteffects using the study case techniques of artificial neural networks (ANN) and support vector regression (SVR).