Machine Learning in Environmental Monitoring

The role of artificial intelligence in the fight against climate change has been increasing in the past couple of years. The onslaught of pollution, amongst other environmental concerns remains one of the world’s most pressing issues. In this article, we’ll look into the application of machine learning in environmental monitoring.

What is Environment Monitoring & Why is it Important?

Environmental monitoring indicates assessment of the environment. Specific tools and techniques measure a series of parameters to determine the quality of the environment, and the impact human activities have on it. The results of this analysis provide a risk and impact assessment report. The main objective is to manage and minimise the impact of an organisation’s activities on the environment. Either to ensure compliance with laws and regulations or to minimise risks of harmful effects on the natural environment.

Even before the threat of climate change became pressing, monitoring the environment has been important. The understanding of how the environment works, allows societies to plan better, from anticipating the coming of a storm to correctly identifying when the weather will shift. But now, with the issue of climate change, it is imperative to get better at monitoring the environment and to react more effectively to drastic changes.

Role of AI in Environmental Monitoring

The technology behind environmental monitoring has evolved quite significantly over the last couple of decades. Previously, the majority of the technology and engineering used revolved around the usage of sensors and specialised observational tools.

Today, these tools are paired with smarter devices that fall under the Internet of Things (IoT). These devices are essentially wired into the internet and are therefore less manual in nature, which is to say they can be controlled remotely in real-time. IoT tools are also paired with AI, meaning they have machine learning algorithms embedded into them.

Data captured by IoT device sensors from a wide variety of environmental conditions can be integrated via the Wireless Sensor Network (WSN), a cloud-based environmental system. In this way, the devices can record, characterise, monitor, and analyse elements in a specific environment.

AI for Air Pollution Monitoring

Historically, air pollution has been one of the most visible markers of climate change. So much so that we have made memes, articles, and news coverage of cities like Beijing or Mumbai covered in a thick fog of dark smoke. In this area, AI can also be applied.

AI can be used to map out air pollution across the world. Using a combination of IoT monitoring devices and ML forecasting algorithms. Imagine a city having a dashboard that provides these insights. This could be incredibly useful for policy-making and introducing interventions.

Moreover, if cities across the world can coordinate their findings, we can have a global view and see which locations are struggling to reduce their air pollution. Cities can also share best practices that might be applicable to the circumstances of different cities.

AI for Noise Pollution Monitoring

Noise pollution is a very different type of pollution. Certainly, not one that drives climate change as clearly as other types of pollution, but it remains a problem nonetheless. According to National Geographic, noise pollution can negatively affect the people’s health as well as wildlife, both on land and at sea.

As such, the application of AI to addressing noise pollution shouldn’t be a surprise. In fact, we at Sparkd, have developed an API to address this issue for one of our clients. In essence, AI algorithms paired with IoT can be trained to assess the nature of the noise, pinpoint the sources, and reveal at which point the noise becomes dangerous. Provided this insight, companies can tweak their practices and governments and local authorities can set guidelines to be followed by companies.

AI for Water Pollution

Last year, in 2021, a team of researchers at the University of Sterling developed a new ML algorithm that improves the remote monitoring of bodies of water. The algorithm has been trained to underline the quality shifts in water caused by either climate change or pollution.

The algorithm takes a meta-learning approach. Taking advantage of data gathered by satellite sensors, this approach provides environmental and industrial companies with an easier means to monitor the environment.This is only one example of AI being used for water pollution. Indeed, there are numerous applications, from assessment, to water treatment, and pollution control.

Last Thoughts

AI in environmental monitoring is one of the many applications of AI in the fight against climate change. We will discuss more applications in the following articles, so stay tuned!

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Author: Michelle Diaz

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