American International Journal of Research and Innovation

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Smart Environmental Monitoring Systems Using Advanced Data Analytics

Author(s) Marco Di Felice
Country United States
Abstract The increasing complexity of environmental challenges, including climate variability, pollution growth, ecosystem degradation, and resource management issues, has created a strong demand for intelligent monitoring systems capable of providing accurate and timely environmental information. Traditional environmental monitoring approaches often depend on manual observations, periodic data collection, and limited analytical capabilities, which may reduce the effectiveness of real-time environmental assessment. Smart environmental monitoring systems have emerged as advanced solutions by integrating sensor technologies, artificial intelligence, Internet of Things platforms, and advanced data analytics for improved environmental intelligence.
This study examines smart environmental monitoring systems using advanced data analytics by analysing their contribution to environmental prediction accuracy, data processing efficiency, monitoring capability, and decision-support performance. The research explores key components of smart monitoring frameworks including IoT-based sensor networks, artificial intelligence algorithms, cloud-based environmental platforms, remote sensing technologies, and predictive analytics models. A simulation-based analytical methodology is developed to compare conventional monitoring approaches with smart environmental monitoring systems using evaluation indicators including monitoring accuracy, data analytics efficiency, environmental risk detection, and decision-support effectiveness.
The findings demonstrate that smart environmental monitoring systems significantly improve environmental assessment capabilities through continuous data collection, intelligent analysis, and predictive modelling. The results indicate that advanced analytics enable more effective identification of environmental patterns, pollution trends, and ecological changes. However, successful implementation requires addressing challenges related to sensor reliability, data quality, technological integration, computational requirements, and system scalability.
Keywords Smart Environmental Monitoring, Data Analytics, Internet of Things, Environmental Intelligence, Artificial Intelligence, Sensor Networks, Predictive Analytics
Field Engineering
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-09-01
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