AI insights on Climate Change
Glaciers shrunken, ice on rivers and lakes breaking apart earlier, plant and animal ranges shifted and trees area unit flowering sooner than usual.
But, Artificial Intelligence for climate action has the potential to scale back the world. Achieving 5% to 10% reduction in carbon dioxide equivalent from global GHG emission and confirming 89 to 99 per cent accuracy in identifying tropical cyclones, weather fronts and atmospheric rivers.
This year’s 16th edition of the Climate Risk Index shows: Signs of intensified temperature changes that have crossed the limits and it cannot be ignored anymore– on any continent or in any region.
Altogether, between 2000–2019 of the span, over 475000 individuals lost their lives as a result of 11000 extreme weather events globally and amounted to around US$ 2.56 trillion in the loss.
Indication of the level of exposure, and vulnerability to extreme weather events, that countries ought to perceive it as a warning, to be ready for more severe events expected in the future.
Expansive new information streams and physical models have brought multiple opportunities to use AI to handle the requirements of a planet on life support. Potential areas in which AI has been responsive; the event of recent climate solutions, land management practices, water security, environmental justice, prediction of air and groundwater pollution, preventing extinction, and optimizing nature for human health and well-being.
So far, Artificial intelligence has demonstrated in:
Predicting, detecting, and mitigating environmental changes:
Early detection of changes could lead to prepare responses, mitigation of bad outcomes, or the ability to incentivize promising responses in predicting new states that we have not seen before, based on prior conditions. Leveraging theoretical models and quantifying uncertainty to critically determine the effectiveness of environmental intelligence.
Climate Study
Machines can analyze big data that’s generated from sensors, gauges, and monitors every day to spot patterns automatically and quickly identify our biggest vulnerabilities and risk zones.
The data from NASA aggregated at Landsat is shared by climate scientists to decision-makers to respond to the impact of the current status of climate changes such as hurricanes, rising sea levels, and higher temperatures etc.
Developing Better Solutions
Artificial intelligence and deep learning assist climate researchers and innovators to test out theories.
One example of this is the IBM project — Green Horizon that studies environmental data and predicts pollution to test and analyze, “what-if” scenarios that involve pollution-reducing strategies.
Green Initiatives
Artificial intelligence and machine learning can help create products and services like consumer-facing AI devices such as smart thermostats and irrigation systems that help conserve resources.
Some of the major projects under Green Initiatives include:
- Carbon Tracker monitoring coal plant emissions with satellite imagery, and using the data to convince the finance industry that carbon plants aren’t profitable and get a better sense of where air pollution is coming from.
- Researchers from Montreal Institute for Learning Algorithms, Microsoft, and ConscientAI Labs used GANs, a type of AI, Cyclegans, that generates statistics for stimulating an AI to train itself to produce images that portray geographical locations or homes before and after extreme weather events like rising sea levels or intense storms.
- SilviaTerra experimenting with AI with satellite imaging to predict the sizes, species, and health of forest trees.
- Google and DeepMind together, developed an AI that would guide themselves how to use only the minimum amount of energy that is necessary to cool down Google’s data centres and has cut down up to 35% and may be able to use them for energy-saving applications in the future.
Opening to new data streams:
The flexibility of AI to integrate environmental data sets by revealing physics-based models as data sources, inclusive of predictive models such as Earth System Models (climate and ecosystem simulators) and the National Water Model, folding into a next-generation decision-support tool.
Better Weather Predictions :
Machines using machine-learning algorithms are being deployed to assess the strengths of models that are used to investigate climate change by reviewing the dozens of models in use and extracting intelligence from them.
Doesn’t AI has negative effects on the climate like Carbon Footprint?
The University of Massachusetts at Amherst report estimated that the power needed to develop and train a neural network was more or less 5 times the common car’s lifespan emissions. So yes, AI will have a carbon footprint, meanwhile considering AI’s carbon footprint, the tremendous price of AI and the real-world outcomes from AI have wear-reducing carbon emissions.
Researchers are creating alternatives to reduce carbon footprint by adopting renewable resources, developing AI once-for-all neural networks to cut back on AI’s carbon footprint.
Looking at how much global climate has changed, going back to “normal” is in the past, hence man should notice and implement some solutions comparatively quickly to overcome the disaster that awaits us.
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