The use of artificial intelligence (AI) in environmental surveillance is on the rise across a range of fields. Here are some examples:
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Streamlining Environmental Audits: AI, coupled with machine learning, streamlines environmental audits by scrutinizing images captured from satellites or drones. This technology bolsters regulatory efficiency and fine-tunes resource distribution for inspections.
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Deforestation Surveillance: AI is harnessed in satellite surveillance to monitor global emissions, deforestation, and the implementation of renewable energy. For example, the World Environment Situation Room (WESR) utilizes AI to dissect intricate environmental datasets for instantaneous insights.
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Biological Surveillance of Freshwater Sources: Particularly, deep learning convolutional neural networks (CNNs), a subset of AI, are employed for real-time surveillance of harmful microbial populations in freshwater sources. This technology expedites the detection and identification process of microbial groups, augmenting current monitoring capabilities.
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Predictive Modelling: AI enables predictive modelling by examining historical data to make precise predictions about future environmental conditions. Machine learning algorithms can pinpoint trends, anomalies, and potential problems that might have been missed earlier.
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Wildlife Preservation: Cameras and drones powered by AI assist wildlife preservation efforts by observing habitats, tracking endangered species, and supplying crucial data for conservation projects. Machine learning algorithms can scrutinize camera footage to recognize species and behaviors.
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Detection and Management of Pollution: AI assumes a critical role in identifying and managing pollution by incessantly monitoring air and water quality using sophisticated sensors. Automated alerts can be triggered when pollutants are identified, facilitating prompt actions to mitigate environmental impact.
AI’s application in environmental monitoring brings advantages such as improved accuracy, real-time surveillance, cost-efficiency, and proactive sustainability initiatives. The amalgamation of AI with IoT (Internet of Things), autonomous environmental robots, and advanced pollution detection systems signifies the prospective direction of AI applications in environmental preservation.
