How Machine Learning and Cloud Computing Decipher Hidden Patterns that Signal Environmental Change


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Within our solar system, planet Earth is a miracle teeming with life in all shapes and forms on land and in the sea. Animals, plants, and elements are supposed to live in harmony under the umbrella of Mother Earth while preserving this rich environment for the next generations to thrive. The well-being of the environment has become not only a necessity but our duty to preserve it. 

In an effort to understand and quantify the effect of humans on global climate, scientists and researchers monitor:

  • weather changes/patterns tracked over time
  • frequency of cataclysmic events
  • geological movement of the continental plates

Space-based tracking of animals using miniature sensors, such as the biosensors implanted in marine life, is an emerging technology that delivers valuable data on animal movement at a near-global scale. Given the sheer size of the datasets collected, machine learning (ML), cloud computing, and storage take center stage in deciphering the hidden patterns that signal environmental change.

We will touch on a couple of topics for this article:

  1. biological observations with animal sensors, space-tracking using a global mobile grid of animal carriers
  2. using biosensors to detect aquatic toxins

Biological observations from animal sensors

The breakthrough in tracking micro and macro movement of animals and converting it into useful data came in September 2020. 

A low-cost Global Positioning System (GPS) tag attached to the back of a Eurasian blackbird that migrated from Belarus to Albania beamed a signal to the International Space Station (ISS) as it passed over its location about 400km above. The tag sent a data packet, just 223 bytes in size, containing GPS location and onboard sensor data that pinpointed the bird’s position and ushered in the epoch of space-based Earth observation and ecological sensing. 

The data was relayed to the International Cooperation for Animal Research Using Space (ICARUS) receiver to be stored, processed, and interpreted. Cloud computing and advanced ML algorithms then help scientists decode hidden patterns in the data and identify biological and environmental changes that help monitor the planet’s health and identify new ways of preserving our environment. 

While not all species are conducive to this type of tracking, scientists have devised new sensors for all life forms, including mammals, reptiles, birds, and fish. The interactions between those species are also being monitored. 

earth day 2022 machine learning environment biosensors diagram

Figure 1: Earth observational model based on animal tracking

Using 4g or 3g battery or solar-powered sensors, this tracking system using low-cost sensors and networked satellites, allows the scientists to capture the position and interpret the behavior of a myriad of animals in near real-time. 

Connecting tens of thousands of animal-attached sensors gives us a new way of understanding and monitoring our planet, helping us devise new ways of combating global climate change, saving many endangered species, and leaving a cleaner planet for the new generation to enjoy and protect. 

Using biosensors to detect marine toxins

Harmful algae blooms (HABs) and marine biotoxins pose an increasing danger to global health, economies, and the environment due to the proliferation of toxin-producing algae. There is a strong link between global warming and the increased frequency of HABs. The toxins produced by these blooms eventually end up in marine animals (fish, shrimp, marine mammals) and can lead to intoxication syndromes in humans. 

Monitoring HABs currently relies on traditional methods such as optical microscopy, a highly time-consuming and labor-intensive process that requires trained personnel and precision microscopes since accurate identification of certain species is difficult. Many samples must be collected, and, in some cases, scanning electron microscopes (SEMs) must be used to properly identify the species present in the seawater. 

To address this issue, in situ monitoring of algae and their associated biotoxins using biosensors is a new method that is catching on. This method is a promising alternative to the laboratory-based analytical methods currently used in toxin monitoring. They also present reliable performance characteristics, are easy to use, and are cost-effective while returning near-real-time results. 

Those new biosensors can be deployed on stationary moorings or autonomous underwater vehicles. The Imaging Flow Cytobot, created by McLane Research Laboratories, Inc., combines high-resolution video imaging with flow cytometry allowing autonomous in situ classification of marine algae. An Environmental Sample Processor (ESP) developed by the Monterey Bay Aquarium Research Institute (MBARI) has been deployed in Monterey Bay, California to detect both the algae species and the neurotoxin domoic acid produced by those species.

We cannot overemphasize the impact of cloud computing on the development of these methods of monitoring the health of our planet. Advanced ML and neural network algorithms are peeking deeper than ever into the gigantic cluster of data provided by the billions of living sentinels around us, deciphering hidden patterns and proving new ways of protecting the earth that provides a warming home for all of us. The future of cloud computing is bright and seems to be increasingly linked to fighting global warming, assisting with decarbonization efforts, helping us be more sustainable, and preserving life on Earth as it is today. The miracle of life must go on, and thousands of computer clusters help us uphold our commitment to a clean and flourishing world.

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