Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Anuradha Kodali and Marcin Szubert and 
                 Kamalika Das and Sangram Ganguly and Josh Bongard",
  title =        "Understanding Climate-Vegetation Interactions in
                 Global Rainforests Through a GP-Tree Analysis",
  booktitle =    "15th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2018",
  editor =       "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and 
                 Penousal Machado and Luis Paquete and Darrell Whitley",
  volume =       "11101",
  series =       "LNCS",
  pages =        "525--536",
  address =      "Coimbra, Portugal",
  month =        "8-12 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Hierarchical
                 modelling, Symbolic regression, Earth science,
                 Nonlinear models",
  isbn13 =       "978-3-319-99252-5",
  URL =          "https://www.springer.com/gp/book/9783319992587",
  DOI =          "doi:10.1007/978-3-319-99253-2_42",
  abstract =     "The tropical rainforests are the largest reserves of
                 terrestrial carbon and therefore, the future of these
                 rainforests is a question that is of immense importance
                 in the geoscience research community. With the recent
                 severe Amazonian droughts in 2005 and 2010 and on-going
                 drought in the Congo region for more than two decades,
                 there is growing concern that these forests could
                 succumb to precipitation reduction, causing extensive
                 carbon release and feedback to the carbon cycle.
                 However, there is no single ecosystem model that
                 quantifies the relationship between vegetation health
                 in these rainforests and climatic factors. Small scale
                 studies have used statistical correlation measure and
                 simple linear regression to model climate-vegetation
                 interactions, but suffer from the lack of comprehensive
                 data representation as well as simplistic assumptions
                 about dependency of the target on the covariates. In
                 this paper we use genetic programming (GP) based
                 symbolic regression for discovering equations that
                 govern the vegetation climate dynamics in the
                 rainforests. Expecting micro-regions within the
                 rainforests to have unique characteristics compared to
                 the overall general characteristics, we use a modified
                 regression-tree based hierarchical partitioning of the
                 space to build individual models for each partition.
                 The discovery of these equations reveal very
                 interesting characteristics about the Amazon and the
                 Congo rainforests. Our method GP-tree shows that the
                 rainforests exhibit tremendous resiliency in the face
                 of extreme climatic events by adapting to changing
  notes =        "PPSN2018 http://ppsn2018.dei.uc.pt

                 This two-volume set LNCS 11101 and 11102 constitutes
                 the refereed proceedings of the 15th International
                 Conference on Parallel Problem Solving from Nature,
                 PPSN 2018",

Genetic Programming entries for Anuradha Kodali Marcin Szubert Kamalika Das Sangram Ganguly Josh C Bongard