Climate Change Adaptation

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This page is about the intersection of climate change adaptation and machine learning. For an overview of climate change adaptation as a whole, please see the Wikipedia page on this topic.
A summary of the different domains within which machine learning can support climate adaptation. Figure from "Tackling Climate Change with Machine Learning."

Climate change adaptation refers to changes that can increase the resilience and robustness of earth and social systems. A system is resilient if it can gracefully recover from climate impacts, and it is robust if it has the impacts themselves are minimal.

Three ways that machine learning can support adaptation are highlighted in the paper "Tackling Climate Change with Machine Learning,"
  • Sounding alarms: Identifying and prioritizing the areas of highest risk, by using evidence of risk from historical data.
  • Providing annotation: Extracting actionable information or labels from unstructured raw data.
  • Promoting exchange: Making it easier to share resources and information to pool and reduce risk.

Machine Learning Application Areas

Background Readings

  • Quinn, J. et al. Computational sustainability and artificial intelligence in the developing world[1] (2014).
  • Gomes, C. et al., Computational sustainability: Computing for a better world and a sustainable future.[2] (2019)
  • Agrawal, A., and Perrin, N. Climate adaptation, local institutions and rural livelihoods. (2009)
  • Shi, L. et al. Roadmap towards justice in urban climate adaptation research. (2016)

Online Courses and Course Materials


Journals and conferences

Societies and organizations

Past and upcoming events

Libraries and Tools


Satellite imagery are used for ecological and social observation. Some public sources include,

There have also been competitions revolving around climate change adaptation issues,

This competition describes an attempt to use mobile money effort to improve financial inclusion and resilience.

Improved disease surveillance and response is an important part of adaptation – here is one competition with this goal in mind.


  1. Quinn, John; Frias-Martinez, Vanessa; Subramanian, Lakshminarayan (2014-09-29). "Computational Sustainability and Artificial Intelligence in the Developing World". AI Magazine. 35 (3): 36. doi:10.1609/aimag.v35i3.2529. ISSN 0738-4602.
  2. Schneider, Sabrina (2019), "The Impacts of Digital Technologies on Innovating for Sustainability", Palgrave Studies in Sustainable Business In Association with Future Earth, Cham: Springer International Publishing, pp. 415–433, ISBN 978-3-319-97384-5, retrieved 2020-08-28