Climate Change Adaptation: Difference between revisions

== 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.