Climate Change Adaptation

Revision as of 03:02, 24 September 2020 by Krisrs1128 (talk | contribs)

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.

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.

A summary of the different domains within which machine learning can support climate adaptation. Figure from "Tackling Climate Change with Machine Learning."[1]
Three ways that machine learning can support adaptation are highlighted in the paper "Tackling Climate Change with Machine Learning,"[1]
  • 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


  • Camera trap image classification
  • Analysis of citizen science data
  • Environmental sensor network analysis


  • Predictive maintenance
  • Risk and vulnerability assessment

Societal Systems

  • Monitoring food supplies
  • Public health
  • Responding to food security


  • Annotating disaster maps
  • Delivering alerts

Background Readings


  • Chapter 20: "Adaptation Planning and Implementation" in the IPCC Fifth Assessment Report (2014)[2]: An overview of current understanding on climate impacts and risks.
  • Computational sustainability and artificial intelligence in the developing world (2014)[3]: A review describing the use of machine learning in problems related to health, food security, and mobility in the developing world.
  • Computational sustainability: Computing for a better world and a sustainable future (2019)[4]: An overview of the goals and techniques used in the field of computational sustainability.

Online Courses and Course Materials


Major conferences

  • ACM Compass: An annual conference focused on computing for sustainable societies. Website here.
  • AI for Good Global Summit: An annual conference organized by the UN ITU. Website here.

Major journals

  • PLOS Responding to Climate Change: A channel from the open access journal PLOS dedicated to responses to climate change. Journal website here.
  • Lancet Health and Climate Change: An initiative by the journal Lancet disseminating research on climate change and public health. Journal website here.

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. 1.0 1.1 Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].
  2. Abeysinghe A, Denton F, Bhadwal S, Burton I, Gao Q, Leal W, Lemos MF, Masui T, O'brien K, Van Ypersele JP, Warner K, and Wilbanks T, Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. 2014
  3. 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.
  4. 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