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. Figure from "Tackling Climate Change with Machine Learning."]]
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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."<ref name=":0">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). [http://arxiv.org/abs/1906.05433 "Tackling Climate Change with Machine Learning"]. ''arXiv:1906.05433 [cs, stat].''</ref>]]
 
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.
 
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,"<blockquote>
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Three ways that machine learning can support adaptation are highlighted in the paper "Tackling Climate Change with Machine Learning,"<ref name=":0" /><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.
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== Machine Learning Application Areas ==
 
== Machine Learning Application Areas ==
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'''Ecology'''
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* Camera trap image classification
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* Analysis of citizen science data
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* Environmental sensor network analysis
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'''Infrastructure'''
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* Predictive maintenance
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* Risk and vulnerability assessment
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'''Societal Systems'''
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* Monitoring food supplies
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* Public health
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* Responding to food security
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'''Crisis'''
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* Annotating disaster maps
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* Delivering alerts
   
 
== Background Readings ==
 
== Background Readings ==
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'''Primers'''
   
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*'''Chapter 20: "Adaptation Planning and Implementation" in the IPCC Fifth Assessment Report (2014)'''<ref>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</ref>: An overview of current understanding on climate impacts and risks.
* Quinn, J. et al. Computational sustainability and artificial intelligence in the developing world<ref>{{Cite journal|last=Quinn|first=John|last2=Frias-Martinez|first2=Vanessa|last3=Subramanian|first3=Lakshminarayan|date=2014-09-29|title=Computational Sustainability and Artificial Intelligence in the Developing World|url=http://dx.doi.org/10.1609/aimag.v35i3.2529|journal=AI Magazine|volume=35|issue=3|pages=36|doi=10.1609/aimag.v35i3.2529|issn=0738-4602}}</ref> (2014).
 
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*'''Computational sustainability and artificial intelligence in the developing world (2014)'''<ref>{{Cite journal|last=Quinn|first=John|last2=Frias-Martinez|first2=Vanessa|last3=Subramanian|first3=Lakshminarayan|date=2014-09-29|title=Computational Sustainability and Artificial Intelligence in the Developing World|url=http://dx.doi.org/10.1609/aimag.v35i3.2529|journal=AI Magazine|volume=35|issue=3|pages=36|doi=10.1609/aimag.v35i3.2529|issn=0738-4602}}</ref>: A review describing the use of machine learning in problems related to health, food security, and mobility in the developing world.
* Gomes, C. et al., Computational sustainability: Computing for a better world and a sustainable future.<ref>{{Citation|last=Schneider|first=Sabrina|title=The Impacts of Digital Technologies on Innovating for Sustainability|date=2019|url=http://dx.doi.org/10.1007/978-3-319-97385-2_22|work=Palgrave Studies in Sustainable Business In Association with Future Earth|pages=415–433|place=Cham|publisher=Springer International Publishing|isbn=978-3-319-97384-5|access-date=2020-08-28}}</ref> (2019)
 
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*'''Computational sustainability: Computing for a better world and a sustainable future (2019)'''<ref>{{Citation|last=Schneider|first=Sabrina|title=The Impacts of Digital Technologies on Innovating for Sustainability|date=2019|url=http://dx.doi.org/10.1007/978-3-319-97385-2_22|work=Palgrave Studies in Sustainable Business In Association with Future Earth|pages=415–433|place=Cham|publisher=Springer International Publishing|isbn=978-3-319-97384-5|access-date=2020-08-28}}</ref>: An overview of the goals and techniques used in the field of computational sustainability.
* 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 ==
 
== Online Courses and Course Materials ==
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== Community ==
 
== Community ==
   
=== Journals and conferences ===
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=== '''Major conferences''' ===
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* '''ACM Compass''': An annual conference focused on computing for sustainable societies. Website [https://acmcompass.org/ here].
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* '''AI for Good Global Summit''': An annual conference organized by the UN ITU. Website [https://www.itu.int/en/ITU-T/AI/2018/Pages/default.aspx here].
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=== Major journals ===
   
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*'''PLOS Responding to Climate Change''': A channel from the open access journal PLOS dedicated to responses to climate change. Journal website [https://channels.plos.org/rtcc here].
*[https://channels.plos.org/rtcc PLOS Responding to Climate Change]
 
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*'''Lancet Health and Climate Change''': An initiative by the journal Lancet disseminating research on climate change and public health. Journal website [https://www.thelancet.com/climate-and-health here].
*[https://acmcompass.org/ ACM Compass]
 
*[https://www.itu.int/en/ITU-T/AI/2018/Pages/default.aspx AI for Good Global Summit]
 
*[https://www.thelancet.com/climate-and-health Lancet Health and Climate Change]
 
   
 
=== Societies and organizations ===
 
=== Societies and organizations ===

Revision as of 03:02, 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."[1]

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,"[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

Ecology

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

Infrastructure

  • Predictive maintenance
  • Risk and vulnerability assessment

Societal Systems

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

Crisis

  • Annotating disaster maps
  • Delivering alerts

Background Readings

Primers

  • 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

Community

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

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. 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