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Climate Change Adaptation: Difference between revisions
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== Machine Learning Application Areas ==
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*'''[[Predictive Maintenance|Predictive maintenance]]''': The increased weather extremes associated with climate change can create increased stresses on physical infrastructure, like roads and power lines. Machine learning can support targeted, just-in-time maintenance by isolating components at risk of near-term failure.
*'''Risk and vulnerability assessment''': Better knowledge of where and on what time scale impacts will be felt can support prioritization of resources for societal adaptation.
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*'''[[Food Security|Food security]]''': By affecting rainfall and the timing of growing seasons, climate change poses a risk to food security. Machine learning can support information gathering around food supply chains, providing early warnings about -- and triggering preventative action around -- famines.
*'''Public health''': Climate change can increase the range of vector-borne disease and exacerbate the severity and frequency of heatwaves. Both pose public health hazards, and machine learning can support risk assessment and outreach to vulnerable populations.
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*'''Annotating disaster maps''': During crisis situations, relief organizations rely on detailed maps -- these are often the most reliable sources of information about the locations of schools, hospitals, and highways, for example. Machine learning can accelerate what are otherwise manual mapping processes.
*'''Delivering alerts''': Machine learning can support situational awareness during crises, distilling large volumes of raw information (e.g., from social media or weather forecasts) into forms that can guide action.
== Background Readings ==
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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.
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== Online Courses and Course Materials ==
== Conferences, Journals, and Professional Organizations ==
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* '''ACM Compass''': An annual conference focused on computing for sustainable societies. Website [https://acmcompass.org/ here].
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== Libraries and Tools ==
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== Data ==
Satellite imagery are used for ecological and social observation. Some public sources include,
* [https://github.com/chrieke/awesome-satellite-imagery-datasets '''awesome-satellite-imagery-datasets''']: A github repository of accessible satellite imagery data.
* [https://github.com/cloudtostreet/MODIS_GlobalFloodDatabase '''Global Flood Database''']: A github repository that includes code and supporting data for the Global Flood Database.
There have also been competitions revolving around climate change adaptation issues,
* [https://app.wandb.ai/wandb/droughtwatch/benchmark '''DroughtWatch''']:
* [https://www.drivendata.co/case-studies/promoting-digital-financial-services-in-tanzania/ '''Promoting Digital Financial Services in Tanzania''']:
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Improved disease surveillance and response is an important part of adaptation – here is one competition with this goal in mind.
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