Climate Change Adaptation: Difference between revisions

*'''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.

* [https://github.com/chrieke/awesome-satellite-imagery-datasets '''awesome-satellite-imagery-datasets''']: A github repository of accessible satellite imagery data.

* [https://app.wandb.ai/wandb/droughtwatch/benchmark '''DroughtWatch''']: Prediction for drought monitoring in Kenya.

* [https://www.drivendata.co/case-studies/promoting-digital-financial-services-in-tanzania/ '''Promoting Digital Financial Services in Tanzania''']: Improving efficiency of money services for improvement of financial inclusion and resilience.

*[https://zindi.africa/competitions/ibm-malaria-challenge '''IBM Malaria Challenge''']: Machine learning disease surveillance and response, which is motivated by the spread of vector borne disease resulting from climate change.