Difference between revisions of "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 [https://en.wikipedia.org/wiki/Climate_change_adaptation Wikipedia page] on this topic.''[[File:Societal-adaptation.png|thumb|A summary of the different domains within which machine learning can support climate 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 [https://en.wikipedia.org/wiki/Climate_change_adaptation Wikipedia page] on this topic.''[[File:Societal-adaptation.png|thumb|A summary of the different domains within which machine learning can support climate adaptation. Figure from "Tackling Climate Change with Machine Learning."]]
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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.
Changes in the atmosphere have impacts on the ground. The expected societal impacts of climate change include prolonged ecological and socioeconomic stresses as well as brief, but severe, societal disruptions. For example, impacts could include both gradual decreases in crop yield and localized food shortages. If we can anticipate climate impacts well enough, then we can prepare for them by asking:
 
 
* How do we reduce vulnerability to climate impacts?
 
* How do we support rapid recovery from climate-induced disruptions?
 
 
A wide variety of strategies have been put forward, from robust power grids to food shortage prediction, and while this is good news for society, it can be overwhelming for an ML practitioner hoping to contribute. Fortunately, a few critical needs tend to recur across strategies – it is by meeting these needs that machine learning has the greatest potential to support societal adaptation. From a high level, these involve
 
   
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Three ways that machine learning can support adaptation are highlighted in the paper "Tackling Climate Change with Machine Learning,"<blockquote>
 
* Sounding alarms: Identifying and prioritizing the areas of highest risk, by using evidence of risk from historical data.
 
* 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.
 
* 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.
 
* Promoting exchange: Making it easier to share resources and information to pool and reduce risk.
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</blockquote>
   
 
== Machine Learning Application Areas ==
 
== Machine Learning Application Areas ==

Revision as of 02:35, 24 September 2020

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

Community

Journals and conferences

Societies and organizations

Past and upcoming events

Libraries and Tools

Data

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.

References

  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