Jump to content

Climate Change Adaptation: Difference between revisions

no edit summary
No edit summary
No edit summary
=== '''Infrastructure''' ===
 
*'''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.
* Predictive maintenance
*'''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.
* Risk and vulnerability assessment
 
=== '''Societal Systems''' ===
 
*'''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.
* Monitoring food supplies
*'''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.
* Public health
* Responding to food security
 
=== '''Crisis''' ===
 
*'''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.
* Annotating disaster maps
*'''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.
* Delivering alerts
 
== Background Readings ==
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.
*
 
There have also been competitions revolving around climate change adaptation issues,
 
* [https://app.wandb.ai/wandb/droughtwatch/benchmark '''DroughtWatch''']: revolvesPrediction aroundfor drought monitoring in Kenya.
* [https://www.drivendata.co/case-studies/promoting-digital-financial-services-in-tanzania/ '''Promoting Digital Financial Services in Tanzania''']: describesImproving anefficiency attemptof tomoney mobile moneyservices effortfor toimprovement improveof financial inclusion and resilience.
* The [https://zindi.africa/competitions/ibm-malaria-challenge '''IBM Malaria Challenge''']: is a competition aroundMachine Improvedlearning 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.
Cookies help us deliver our services. By using our services, you agree to our use of cookies.