Computational Sustainability

98 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Machine Learning that Matters (2012, background): “The growing area of computational sustainability (Gomes, 2009) seeks to connect ML advances to real-world challenges in the environment, economy, and society.”
Tackling Climate Change with Machine Learning (2019, background): “y, beyond aiding adaptation by prioritizing at-risk environments, the design of effective methods for ecosystem monitoring will support the basic science necessary to shape adaptation in the long-run [11, 14, 532]. Monitoring biodiversity High Leverage Accurate estimates of species populations are t”
When the implication is not to design (technology) (2011, background): “Computational research on sustainability often uses computational methods to approach environmental problems, e.g., modeling biodiversity [15].”
A global predictive model of carbon in mangrove soils (2014, methodology): “ML methods have a long history in the field of medical biosciences (Kononenko 2001) and are rapidly gaining ground in several fields ranging from economics (Varian 2013), to conservation biology and ecology (Gomes 2009, Dietterich 2009).”