How can machine learning be used to address environmental concerns?
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Machine learning has the ability to help humans tackle one of the most urgent and complex challenges of our time: the environment. By deploying its skillset in analyzing data and making predictions, researchers, scientists and policymakers can harness the algorithmic power of machine learning for good. Here are some ways machine learning can contribute to climate and environmental solutions.?
1. Monitoring and measuring: Machine learning can help us collect, process and analyze large and diverse datasets on the state and changes of the Earth's systems, such as the atmosphere, oceans, land and biodiversity. For example, algorithms can enhance the accuracy and efficiency of remote sensing, such as satellite imagery and radar, to detect and quantify phenomena like deforestation and greenhouse gas emissions. Machine learning can also help us integrate and synthesize data from multiple sources and scales, such as sensors, surveys and models. This data can then be used to generate indicators and forecasts of environmental quality and risk. Upon application, machine learning? can help us track and predict issues such as air pollution, water scarcity, natural disasters and disease outbreaks.
2. Mitigation and adaptation: By enabling more sustainable practices in sectors like energy and transportation, machine learning can help reduce the drivers of climate change and environmental degradation. Machine learning can optimize the supply and demand of renewable energy, such as wind and solar, by forecasting weather and electricity prices, as well as controlling smart grids and batteries. It can also improve the efficiency and safety of transportation through autonomous vehicles, smart traffic management and route planning. Agriculture can also become more sustainable through practices like precision farming and crop monitoring.?
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3. Innovation and transformation: Machine learning models can accelerate the design and testing of new materials, such as biofuels and batteries. Through creating new digital and data-driven services, machine learning can also raise awareness around specific issues and incentivize a change in behavior from consumers and communities.?
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This article was edited by LinkedIn News Editor Felicia Hou and was curated leveraging the help of AI technology.
Software Engineering @ Planet Farms | Machine Learning & Robotics
1 年Environmentalists: "We should use Artificial Intelligence to fight climate change." ?? Artificial Intelligence: "We should use nuclear to buy us more time to find better solutions."?? Environmentalists: "You are wrong. The answer is more money in wind and solar." ??
Energy Advisor @ EnergySage
1 年Agrivoltaics optimizations! Everything from water usage, sun exposure, energy production, crop yield, and more can benefit from machine learning!
Climate Justice Advocate | Professional Wellbeing Practitioner | Creative Intellect
1 年ML should be used for identifying zero-waste methods. Reuse of waste streams is where environmental consciousness will bridge the #renewable gap. #environmentalconsciousness #reuserevolution #lightnessofbeing #mindmovementandadvocacy #journeywithlightness
AI, ML, Sustainability, Ecology, Climate Science, Image Analysis, Statistics
1 年We have used ML and 8 band satellite imagery to map the presence of open water wetlands in Hawaii at the 5m scale. More info is here: https://github.com/marireeves/WetlandsMappingHawaii. We also have used it to predict areas of higher risk of trace metal transport from paved versus gravel roads in the Kenai Peninsula of Alaska. Publication is here: https://www.researchgate.net/publication/323404836_Predicting_risk_of_trace_element_pollution_from_municipal_roads_using_site-specific_soil_samples_and_remotely_sensed_data I also just learned about a group @skytruth using satellite imagery and AI/ML to detect oil spills in the ocean. They are here: https://skytruth.org/ with the link to the tool here: https://skytruth.org/cerulean/ Cool stuff!
Vice President, National Director Resilience and Sustainability Strategy ? Business Growth Strategy ? Organizational Change ? Inclusion & Equity ? Partnerships ? Passionate about integrity in business
1 年ML can find the lowest carbon concrete admixture available, which can then be motified for local conditions, project type, and local manufacturing capabilities. Saw a great presentation on this at Greenbuild this year, and this process is applicability for many other building products: https://www.eventscribe.net/2022/Greenbuild/fsPopup.asp?efp=TkVMS1hITEwxNjU3Mg&PresentationID=1093728&rnd=0.301948&mode=presinfo