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Enhancing Environmental Monitoring for Sustainable Development
Abstract
This paper explores the enhancement of environmental monitoring for sustainable development through AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional environmental monitoring practices, including air quality assessment, water quality management, and biodiversity conservation. The study highlights the application of AI techniques such as remote sensing, data fusion, and predictive modeling in analyzing environmental data, detecting trends, and forecasting environmental changes. Additionally, it discusses the integration of AI with sensor networks, satellite imagery, and geographic information systems (GIS) to enable real-time monitoring, early warning systems, and decision support tools for environmental management. The paper also addresses challenges such as data interoperability, model validation, and community engagement in the deployment of AI-driven engineering solutions in environmental monitoring. It emphasizes the importance of interdisciplinary collaboration, stakeholder engagement, and policy support in leveraging AI's potential to enhance environmental sustainability and promote ecosystem resilience.
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References
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