Climate Change Adaptation

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.

Background Readings

• Quinn, J. et al. Computational sustainability and artificial intelligence in the developing world (2014).
• Gomes, C. et al., Computational sustainability: Computing for a better world and a sustainable future. (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)

Community

Journals and conferences

• PLOS Responding to Climate Change
• ACM Compass
• AI for Good Global Summit
• Lancet Health and Climate Change

Data

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

• awesome-satellite-imagery-datasets: A github repository of accessible satellite imagery data.

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

• DroughtWatch revolves around drought monitoring in Kenya.
• Promoting Digital Financial Services in Tanzania describes an attempt to mobile money effort to improve financial inclusion and resilience.
• The IBM Malaria Challenge is a competition around Improved disease surveillance and response, which is motivated by the spread of vector borne disease resulting from climate change.

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.