Climate Change Adaptation
Revision as of 18:44, 19 November 2020 by 2600:1700:6804:db0:899a:bd93:99e9:51f7 (talk)
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.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
- 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.
- Monitoring food supplies: 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.
- 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.
- Chapter 20: "Adaptation Planning and Implementation" in the IPCC Fifth Assessment Report (2014): An overview of current understanding on climate impacts and risks.
- Computational sustainability and artificial intelligence in the developing world (2014): 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): An overview of the goals and techniques used in the field of computational sustainability.
Online Courses and Course Materials
- 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.
- 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
Libraries and Tools
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: Prediction for drought monitoring in Kenya.
- Promoting Digital Financial Services in Tanzania: Improving efficiency of money services for improvement of financial inclusion and resilience.
- IBM Malaria Challenge: Machine learning disease surveillance and response, which is motivated by the spread of vector borne disease resulting from climate change.
Improved disease surveillance and response is an important part of adaptation – here is one competition with this goal in mind.
- ↑ 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].
- ↑ 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
- ↑ 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.
- ↑ 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