The Role of Machine Learning in
Environmental Sustainability
Machine learning and AI are starting to find roots in almost every aspect of the modern world. We previously discussed how AI and ML are being used to predict natural disasters and relief models, something that is a part of a much larger initiative; the role of machine learning, and AI in environmental sustainability.
In 2018, Intel reported that roughly 74% of 200 environmental sustainability business decisions are made with AI and ML models at their back.
Today, one of the biggest threats to humanity as a whole is the climate change we’re looking at. Global warming has resulted in an increase in the number of natural disasters. 20% of all species on Earth are facing extinction — a figure that is likely to rise if nothing is done about it.
AI and ML have helped environment-researchers find ways to identify tropical cyclones, weather fronts precipitation trends, and more, that humans won’t be able to identify on their own. Machine learning, drones, and IoT (internet of things) are coming together not just to predict disasters, but also prevent damage and stress-factors on land, air, and water.
There are also several AI and ML-based initiatives in the works toward reducing energy usage throughout the globe, specifically in the U.S. by 12% to 22%.
Here, we will discuss how ML and AI combine to improve the potential of environmental protection.
Using AI & ML for Energy
Today, AI is used more and more in attempts to alternate between renewable and non-renewable energy. The goal is to incorporate renewable energy as much as possible into the grid and handle the biggest problem with renewable; power fluctuations.
This is done by using Machine Learning to identify where and when any vulnerabilities (fluctuations) in the renewable sources will occur. The research found roots in Stanford University (SLAC National Accelerator Laboratory). The same concept as the detection of natural disasters is used.
Any and all failures are forecasted by AI and continuously improved upon by ML algorithms. As soon as failures are identified, they are either rectified beforehand or handled as quickly as possible to ensure minimal power loss.
The system first studies grids and analyses data received from renewable power sources. The sources themselves, battery storage, and performance is measured along with satellite imagery. Any overgrowing trees that may hit power lines or migrating birds are identified and AI then takes action to mitigate said threats with minimal human involvement.
As for efficiency, companies operating wind turbines are using AI to find more efficient propeller angles and turbines to produce more electricity from each rotation. This is particularly helpful for large wind farms where the first row of turbines disturbs the wind, decreasing the efficiency of the rest. AI studies wind speed and direction to determine the most efficient placement and alignment of each propeller. ML then studies these new adjustments.
Decreasing Carbon Emissions & Life in Cities
There are two ways to improve life in the city when it comes to pollution levels:
1. Shift to renewable energy or greener solutions, or
2. Increase energy efficiency
The incorporation of AI and ML into the grid isn’t just a matter of helping improve efficiency from renewables, but also existing modes of energy generation. AI can predict an increase in energy demand, which in turn can help renewable energy companies improve energy protection.
When it comes to improving life in major cities, AI systems can help governments oversee potential impacts from disasters or other dangerous areas to improve urban planning. This includes creating an “urban dashboard” that includes real-time data on energy and water use.
IBM has implemented its Green Horizon project, which forecasts increases in air pollution and tracks the source of this pollution. Although not perfect yet, the system also gives actionable strategies to deal with these sources.
Improving Agricultural Capabilities — Smart Agriculture
The Earth is 3.1 degrees Centigrade hotter than it was supposed to be in 2020. These hotter temperatures have impacted agriculture adversely. AI and ML have opened new avenues here as well, though.
Sensors in agricultural fields — specifically in the US and Europe — monitor the moisture level in the soil and its temperature with failsafe measures in case either of the two factors go out of hand. There is also a measure of soil composition, where humans are alerted in case of any problems.
The goal is to ensure increased production.
Automatic AI systems also know the best time to sow seeds and harvest them, decreasing human intervention as much as possible. Identifiers are included in ML programs that help AI understand diseased crops, when they need to be sprayed, and when they need to be rooted out.
Cleaning Out Oceans & Protecting Them
Machine learning has found its way into satellites as well, thanks to the Ocean Data Alliance. The goal is ocean exploration and waste management. One way of doing that is by giving decision-makers insightful data about transport ships and their routes.
Other industries such as ocean mining and fishing are also being advised by ML and AI systems up there.
When it comes to protecting ocean life, these satellite images transmit data about coral bleaching, garbage patches, or disease outbreak in marine life and send it to relevant authorities so that they can act accordingly.
Another aspect where artificial intelligence helps keep marine life safe is by tracking and, in many cases, predicting the spread of invasive species and tracking ocean currents.
AI and machine learning are proving to be some of the best tools we have when it comes to environmental sustainability. From the prediction of extreme events all the way to life preservation and energy efficiency, machine learning has found roots in it all.
The prime factor that has made both AI and ML so effective in environmental sustainability is the fact that Machine Learning Engineers can convert real-world complexities such as climate changes, atmospheric pressure, ocean dynamics, life, and more, into figures and calculative models. These models then give us simulations, which in turn, provide valuable insights to decision-makers to help make an impact.
