Machine Learning In Environmental Monitoring

Jan 19

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Environmental monitoring (weather patterns, air particulate matter, greenhouse gases, and so much more) is more difficult to conduct than you might think. Although we get weather information as soon as we tap into our Weather App, collecting such data is nearly impossible due to the surface area that needs to be assessed at each second of the day. Additionally, general sensors, lab analysis, and engineering systems are all facing lower funding and lower supply. However, machine learning is becoming the savior for environmental labs, facilities, and companies.

Machine Learning 101:

Machine Learning has taken over almost every single aspect of our lives, down to the appointments being scheduled to email outline prompts that I know you put into ChatGPT. If you aren't quite sure what machine learning refers to, it is a form of artificial intelligence that allows algorithms to recognize patterns from large datasets and refer decisions or predications to you (learning from examples rather than explicit programming). This dynamic quality is crucial to understand the ever-changing complexity of our weather, oceans, and pollutants on our planet.

Real World Applications:

Our Forests: Predictive models such as the WWF's Forest Foresight are used to study the amount of deforestation the Amazon forest has encountered through satellite data. The rate at which trees are being cut down will allow the algorithms to predict the acres of forest that we have left, prompting quicker reforestation efforts from countries.

Predictions made by Forest Foresight in June 2021

Our Air: Ho Chi Minh City uses a multi-output machine learning model named N-Beats that forecasts concentrations of different pollutants such as carbon monoxide, nitrogen dioxide, ozone, etc. This allows for greater transparency surrounding greenhouse gases but also to help promote action in lowering air pollution in the city.

Our Water: United States researchers and agencies use machine learning models combined with satellite imagery to predict harmful algae blooms in Lake Erie. These models analyze water temperature, nutrient runoff, sunlight exposure, and historical bloom patterns to predict when and where toxic algae blooms may appear. This crucial effort helps to preserve drinking water supplies and even reduce potential ecological damage in situations where physical sensors can't acutely provide enough information.

My Take

The implementation of machine learning is inevitable and already in motion across almost every industry, and for good reason. It allows companies to use more data at a faster rate than ever before to create new solutions. For environmental systems, this speed and scale can be the difference between reacting too late and acting in time. When climate events are becoming more extreme and less predictable, waiting for perfect data is no longer an option. Machine learning helps bridge the gap between what we can measure and what we urgently need to know. This means earlier wildfire warnings, more precise pollution alerts, and smarter conservation strategies. In a world where environmental funding isn't guaranteed, using existing data more intelligently is crucial.

But let's be clear. The effects of machine learning are not all positive. The amount of water that data centers use is alarming and growing exponentially- not to mention the energy required to train these models and data bias. However, despite these setbacks, we can still become more intentional with current rate of innovation. With cleaner energy-powered data centers, more efficient models, and stronger collaboration between policymakers and scientists, we can ensure that machine learning is an overall positive for the planet. When applied responsibly, these tools can help reshape how we interact with our environment.