UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS

Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and ability to predict such events through the mining of severe weather data.

Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally and spatially varying attributes and relationships. We introduce an augmented version of the Spatiotemporal Relational Random Forest, which is a Random Forest that learns with spatiotemporally varying relational data. Our algorithm maintains the strength and performance of Random Forests but extends their applicability, including the estimation of variable importance, to complex spatiotemporal relational domains.