How Machine Learning Can Help in Environment Protection?
Machine learning is the core of artificial intelligence. It enables AI to evolve and keep adapting to new changes. This form of learning uses algorithms to learn from itself and quickly reach a self-sufficient performance level. Machine learning, with its vast potential and power, can now help experts in environment protection and nature conservation.
Natural disasters, global warming and human impact affect the environment in various ways. Water infrastructure can become polluted during storms, and animals are in constant danger from the climate crisis and human poaching. Though it can’t halt any of these instances, machine learning can reduce their frequency and the harm they cause.
Let us explore the major applications of machine learning in environment protection:
1. Facility Inspection
When it comes to storms and natural disasters, rain, snow or strong winds can pick up toxic materials. For instance, a hurricane will carry waste from agricultural farms that eventually mixes with waterways. Humans and animals depend on water systems as part of an area’s municipal infrastructure, keeping systems clean and healthy.
If animal waste or toxic metals mix with water, it creates severe environmental problems. Ecosystems and animals can die off from the pollutants, and toxic substances can get into the water supply, eventually harming human health. Budget constrictions can prevent experts from fixing the infrastructure or cleaning up.
Instead, machine learning provides fast and easy preventive measures for environmental monitoring. Inspections are a critical part of keeping facilities of all kinds clean and running efficiently. However, the Environmental Protection Agency can’t inspect every facility each year. This obstacle leads to a lack of regulation.
Machine learning can take into account several factors about any location. Data scientists use the data on industry, specific location, inspection history, material usage and current protocols to predict and then secure the facility. The system then prioritizes which areas are more of an immediate threat to the environment.
That way, regulators can more efficiently optimize each location, making them less harmful to the surrounding area. Their waste, for instance, will no longer be a threat during storms or heavy rainfall.
2. Animal Protection
Animals are critical parts of ecosystems around the globe. They balance the environment, protecting it and using it as a home. Various threats, though, can change that dynamic quickly. Poaching and deforestation are two of the most significant environmental concerns for animals. Machine learning can help here, too.
Machine learning can categorize animals based on pictures alone. When environmentalists or wildlife specialists capture images or photos, they can have their machine learning systems process the data and accurately classify the animals. This step helps with tracking populations and behaviors to properly protect them.
To protect animals against poaching, experts can add a game-theory component to the algorithms. With input about what animals have high values and are rare, combined with behavior models and computing, machine learning can better understand poacher strategy. It will ultimately predict which animals to protect.
3. Deforestation
Machine learning can then extend to predicting deforestation. Trees are vital for reducing carbon and producing oxygen — without them, the overall climate crisis would worsen. However, they also make homes for countless animals in forests, tropical areas and just about everywhere else on the planet.
Deforestation occurs when the value of land is ideal for development. The process wipes out natural resources, harms animals and ruins ecosystems. With the same game-theory mechanics, machine learning can use factors of land like size, altitude, proximity to resources and location to understand the value. If it has a high value, environmentalists know to protect it from developers.
Future Changes
Though machine learning and AI are powerful as they are now, tech can always use some improvements. For instance, using machine learning for facility inspection prioritization can have some drawbacks. Those lower on the list — or those who know they won’t need an inspection — may become laxer with protocols and end up polluting or harming the environment more.
However, these steps are the first of many for AI in environmental sustainability. After providing better ways to regulate and track changes, adapting these protocols on a broader scale should come next.
Emily Folk is freelance writer and blogger on topics of renewable energy, environment and conservation. You may read more of her work on http://www.conservationfolks.com. Follow her on Twitter @EmilySFolk
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