IN51A-1799
Regression based modeling of vegetation and climate variables for the Amazon rainforests
Friday, 18 December 2015
Poster Hall (Moscone South)
Anuradha Kodali, Organization Not Listed, Washington, DC, United States, Ankush Khandelwal, University of Minnesota Twin Cities, Minneapolis, MN, United States, Sangram Ganguly, NASA Ames Research Center, Moffett Field, CA, United States, Joshua Bongard, University of Vermont, Burlington, United States and Kamalika Das, University of Maryland Baltimore County, Baltimore, MD, United States
Abstract:
Both short-term (weather) and long-term (climate) variations in the atmosphere directly impact various ecosystems on earth. Forest ecosystems, especially tropical forests, are crucial as they are the largest reserves of terrestrial carbon sink. For example, the Amazon forests are a critical component of global carbon cycle storing about 100 billion tons of carbon in its woody biomass. There is a growing concern that these forests could succumb to precipitation reduction in a progressively warming climate, leading to release of significant amount of carbon in the atmosphere. Therefore, there is a need to accurately quantify the dependence of vegetation growth on different climate variables and obtain better estimates of drought-induced changes to atmospheric CO2. The availability of globally consistent climate and earth observation datasets have allowed global scale monitoring of various climate and vegetation variables such as precipitation, radiation, surface greenness, etc. Using these diverse datasets, we aim to quantify the magnitude and extent of ecosystem exposure, sensitivity and resilience to droughts in forests. The Amazon rainforests have undergone severe droughts twice in last decade (2005 and 2010), which makes them an ideal candidate for the regional scale analysis. Current studies on vegetation and climate relationships have mostly explored linear dependence due to computational and domain knowledge constraints. We explore a modeling technique called symbolic regression based on evolutionary computation that allows discovery of the dependency structure without any prior assumptions. In symbolic regression the population of possible solutions is defined via trees structures. Each tree represents a mathematical expression that includes pre-defined functions (mathematical operators) and terminal sets (independent variables from data). Selection of these sets is critical to computational efficiency and model accuracy. In this work we investigate appropriate function and terminal set choices for the symbolic regression based modeling of the effects of climate on Amazon vegetation. Additionally, we compare the predictive capability of the symbolic regression based model to baseline techniques such as linear regularized regression and support vector regression.
