Artificial Intelligence for Real-Time Monitoring of Global Environmental Change

David Touretzky

Artificial Intelligence for Real-Time Monitoring of Global Environmental Change

Author(s)	David Touretzky
Country	United States
Abstract	Global environmental change is characterised by increasingly complex interactions among climate systems, ecosystems, atmospheric conditions, land-use patterns, oceans, and human activities. Traditional environmental monitoring approaches often depend on periodic observations, manual data collection, and fragmented analytical systems, which can limit the speed at which environmental changes are detected and interpreted. Artificial Intelligence (AI), combined with satellite remote sensing, Internet of Things (IoT) sensors, autonomous observation systems, and high-performance computing, provides new opportunities for near-real-time environmental monitoring. This study examines the application of AI for real-time monitoring of global environmental change and evaluates its potential contribution to climate observation, biodiversity monitoring, deforestation detection, pollution assessment, disaster management, water-resource monitoring, and ecosystem analysis. A qualitative and conceptual research methodology is adopted through a review of AI-based environmental monitoring approaches. The study examines machine learning, deep learning, computer vision, time-series modelling, anomaly detection, and multimodal AI techniques. The analysis indicates that AI can accelerate environmental data processing, identify patterns across large datasets, detect anomalies, improve forecasting, and support rapid environmental decision-making. However, challenges involving data quality, model generalisation, computational requirements, explainability, interoperability, uncertainty, and unequal access to monitoring technologies remain significant. The study proposes an integrated AI-environmental monitoring framework connecting data acquisition, AI processing, real-time detection, forecasting, human interpretation, and policy response. It concludes that AI can become a critical component of global environmental intelligence when combined with reliable observation networks, scientific expertise, transparent methodologies, and responsible environmental governance.
Keywords	: Artificial Intelligence, Environmental Monitoring, Climate Change, Remote Sensing, Machine Learning, Deep Learning, Environmental Intelligence, Real-Time Monitoring, Satellite Data, Sustainability.
Field	Engineering
Published In	Volume 4, Issue 6, November-December 2022
Published On	2022-11-15

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American Journal of Advanced Multidisciplinary Research and Innovation

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Artificial Intelligence for Real-Time Monitoring of Global Environmental Change

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