American Journal of Advanced Multidisciplinary Research and Innovation

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Smart Environmental Monitoring Through Artificial Intelligence and Sensor Networks

Author(s) James Paul Gee
Country United States
Abstract Environmental degradation, climate change, rapid urbanisation, industrial pollution, and biodiversity loss have intensified the demand for intelligent environmental monitoring systems capable of providing real-time, accurate, and data-driven insights. Traditional environmental monitoring approaches often rely on manual sampling, periodic observations, and isolated sensing infrastructures, which limit responsiveness and predictive capabilities. Recent advances in Artificial Intelligence (AI), Wireless Sensor Networks (WSNs), Internet of Things (IoT), Machine Learning (ML), Deep Learning (DL), Cloud Computing, Edge Computing, Big Data Analytics, Satellite Remote Sensing, and Digital Twin Technology have enabled the development of smart environmental monitoring systems that continuously collect, analyse, and interpret environmental data for intelligent decision-making.
This study presents a comprehensive analysis of Smart Environmental Monitoring Through Artificial Intelligence and Sensor Networks. A qualitative analytical research methodology based on secondary data is employed to investigate AI-enabled environmental sensing, intelligent data analytics, pollution prediction, climate monitoring, ecosystem management, disaster early warning systems, water quality assessment, and sustainable resource management. The research evaluates how AI-powered sensor networks improve environmental awareness, predictive analytics, operational efficiency, and policy formulation.
The findings indicate that intelligent sensor networks significantly enhance air quality monitoring, water resource management, forest conservation, precision agriculture, disaster risk reduction, and climate resilience. Machine learning and deep learning algorithms improve anomaly detection, predictive modelling, environmental forecasting, and automated decision support. Furthermore, integrating Blockchain, Geographic Information Systems (GIS), Unmanned Aerial Vehicles (UAVs), and Digital Twins strengthens environmental transparency, data integrity, spatial intelligence, and sustainable ecosystem management.
Despite these opportunities, challenges remain concerning sensor reliability, energy efficiency, cybersecurity, interoperability, data privacy, infrastructure costs, and regulatory governance. Future research should investigate AI-driven autonomous environmental monitoring, quantum-enhanced climate modelling, explainable environmental AI, federated sensor intelligence, and sustainable green computing architectures.
The study concludes that Artificial Intelligence integrated with intelligent sensor networks provides a scalable, adaptive, and sustainable framework for real-time environmental monitoring, supporting climate resilience, ecological conservation, and evidence-based environmental governance.
Keywords Artificial Intelligence, Environmental Monitoring, Sensor Networks, Internet of Things, Wireless Sensor Networks, Machine Learning, Deep Learning, Climate Monitoring, Smart Environment, Sustainable Development
Field Engineering
Published In Volume 2, Issue 2, March-April 2020
Published On 2020-04-23
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