Leveraging AI for Environmental Solutions addressing Climate Change, Pollution Monitoring and Sustainable Resource Management
Noreen Sher AKBAR, Ghanwa BATOOL, Shahan SIDDIQUI, Muhammad Bilal HABIB
Abstract. Artificial Intelligence (AI) has emerged as a transformative tool in addressing critical environmental challenges, including climate change, pollution monitoring, and sustainable resource management. This review synthesizes recent advancements and applications of AI techniques in these domains, highlighting key findings and identifying future research directions. In climate science, AI has significantly enhanced the accuracy of climate models and optimized renewable energy deployment, contributing to effective mitigation strategies. For pollution monitoring, AI-driven systems offer real-time detection and analysis, surpassing traditional methods in precision and scalability. In sustainable resource management, AI optimizes agricultural practices, forest conservation, and water resource allocation, promoting efficiency and sustainability. Despite these promising advancements, challenges such as data quality, model interpretability, integration with existing systems, and ethical considerations remain.
Keywords
Artificial Intelligence, Convolutions Networks, Environmental Solutions, Energy Efficiency, Sensor Networks, Data Fusion, Pollution Detection, Climate Forecasting, NASA
Published online 2/25/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Noreen Sher AKBAR, Ghanwa BATOOL, Shahan SIDDIQUI, Muhammad Bilal HABIB, Leveraging AI for Environmental Solutions addressing Climate Change, Pollution Monitoring and Sustainable Resource Management, Materials Research Proceedings, Vol. 48, pp 892-902, 2025
DOI: https://doi.org/10.21741/9781644903414-97
The article was published as article 97 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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