Monitoring the Heartbeat of the Amazon Rainforest With AI
Understanding the Client
A leading company in the natural resources and mining sector, playing a significant role in the global supply chain of minerals and metals. As a major producer of iron ore, nickel, coal, copper, and other materials, the company is one of the largest mining organizations in the world.
Context & Challenges
To preserve the natural environment, the client company committed to voluntarily protecting and recovering more than 1.2 million acres of forest in Brazil by the year 2030. But to achieve this goal, the client needed to rethink its approach to sustainability. In its previous state, all environmental monitoring was conducted manually, dispatching field teams into remote areas to collect samples and download data, which was then brought back to the lab for analysis. This process was inefficient and potentially dangerous, exposing employees to field risks and preventing the client from responding quickly to critical situations.
Solution
Using a combination of data analytics, IoT technology, and machine learning algorithms, a solution was developed to proactively monitor environmental variables across the Amazon Rainforest, including air quality, water quality, forest fire detection, and more. Real-time data is collected from 35 environmental monitoring stations, spread across the Amazon, and stored in a central system. The system notifies the Environmental Control Center when abnormal situations arise, allowing field teams to be dispatched quickly and safely, minimizing negative environmental impact.
Results
- Minimized environmental impact by identifying and addressing abnormalities more efficiently
- Reduced response times through automated reports and alarms, improving data traceability, and enhancing information consistency
- Created a culture of data-driven innovation with the potential for application in other locations around the world
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