- author = "Kamalika Das",
- title = "Using Machine Learning to Study the Effects of Climate on the Amazon Rainforests",
- booktitle = "NASA Machine Learning Workshop 2017",
- year = "2017",
- editor = "Michael Lowry",
- address = "Moffett Field, California, USA",
- month = "29-31 " # aug,
- keywords = "genetic algorithms, genetic programming, educational timetabling, construction heuristics, hyper-heuristics",
- bibsource = "OAI-PMH server at ntrs.nasa.gov",
- identifier = "Document ID: 20170012209",
- oai = "oai:casi.ntrs.nasa.gov:20170012209",
- broken = "http://hdl.handle.net/2060/20170012209",
- broken = "https://ti.arc.nasa.gov/events/machinelearningworkshop2017/invitedspeakers/kamalika/",
- broken = "https://ti.arc.nasa.gov/news/Das-ITNG-talk-2018/",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Das_2017_NASAmlw.pdf",
- abstract = "The Amazonian forests are a critical component of the global carbon cycle, storing about 100 billion tons of carbon in woody biomass, and accounting for about 15 of global net primary production and 66 of its inter-annual variability. There is growing concern that these forests could succumb to precipitation reduction in a progressively warming climate causing extensive carbon release and feedback to the carbon cycle. Contradicting research, on the other hand, claims that these forests are resilient to extreme climatic events. In this work we describe a unifying machine learning and optimisation based approach to model the dependence of vegetation in the Amazon on climatic factors such as rainfall and temperature in order to answer questions about the future of the rainforests. We build a hierarchical regression tree in combination with genetic programming based symbolic regression for quantifying the climate-vegetation dynamics in the Amazon. The discovered equations reveal the true drivers of resilience (or lack thereof) of these rainforests, in the context of changing climate and extreme events.",
-
notes = "Apr 2023 gone
https://ti.arc.nasa.gov/events/machinelearningworkshop2017/
May 2018 This requested resource (Online NTRS full-text PDF) is no longer available from NTRS. help@sti.nasa.gov
Also known as \cite{oai:casi.ntrs.nasa.gov:20170012209} See also https://myemail.constantcontact.com/NAMS-Newsletter-February-2019.html?soid=1129123412805&aid=Xq9IV_IXl1U",
