AI-Powered Environmental Monitoring and Conservation Strategies
AI-Powered Environmental Monitoring and Conservation Strategies |
||
|
||
© 2024 by IJETT Journal | ||
Volume-72 Issue-8 |
||
Year of Publication : 2024 | ||
Author : Ranjana Dahake, Kalpana Metre, Namita Kale, Dr. B. J. Dange, Dr. Nitin Mahankale |
||
DOI : 10.14445/22315381/IJETT-V72I8P101 |
How to Cite?
Ranjana Dahake, Kalpana Metre, Namita Kale, Dr. B. J. Dange, Dr. Nitin Mahankale, "AI-Powered Environmental Monitoring and Conservation Strategies," International Journal of Engineering Trends and Technology, vol. 72, no. 8, pp. 1-7, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I8P101
Abstract
In today's society, environmental conservation and sustainable resource management are critical. This article investigates Artificial Intelligence's (AI) transformational potential in boosting environmental monitoring and conservation initiatives. They can transform data gathering, analysis, and decision-making processes by leveraging AI capabilities, thereby contributing to the preservation of the planet's biodiversity and natural ecosystems. They investigate the use of Artificial Intelligence (AI) technologies, such as machine learning and sensor networks, to improve the accuracy and efficiency of environmental data collecting. Case examples show how AI may be used to optimize resource allocation, risk assessment, and adaptive management tactics for conservation programs. However, this trip is not without difficulties, and ethical questions must be addressed. The findings highlight the critical importance of accomplishing long-term environmental management. As Artificial Intelligence (AI) advances, its use in environmental science provides new possibilities for preserving the planet's future and creating a healthy coexistence between people and nature.
Keywords
Artificial Intelligence (AI), Environmental Conservation, Sustainable Resource Management, Biodiversity, Ethical Considerations.
References
[1] Mahzarin R. Banaji, Susan T. Fiske, and Douglas S. Massey, “Systemic Racism: Individuals and Interactions, Institutions and Society,” Cognitive Research: Principles and Implications, vol. 6, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Lorenzo Belenguer, “AI Bias: Exploring Discriminatory Algorithmic Decision-Making Models and the Application of Possible Machine-Centric Solutions Adapted from the Pharmaceutical Industry,” AI and Ethics, vol. 2, pp. 771-787, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Josie Carwardine et al., “Avoiding Costly Conservation Mistakes: The Importance of Defining Actions and Costs in Spatial Priority Setting,” PLoS ONE, vol. 3, no. 7, pp. 1-6, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Josh Cowls et al., “The AI Gambit: Leveraging Artificial Intelligence to Combat Climate Change-Opportunities, Challenges, and Recommendations,” AI and Society, vol. 38, pp. 283-307, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Matthew F. McCabe et al., “The Future of Earth Observation in Hydrology,” Hydrology and Earth System Sciences, vol. 21, no. 7, pp. 3879-3914, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Melese Genete Muluneh et al., “Impact of Climate Change on Biodiversity and Food Security: A Global Perspective-A Review Article,” Agriculture and Food Security, vol. 10, pp. 1-25, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Rohit Nishant, Mike Kennedy, and Jacqueline Corbett, “Artificial Intelligence for Sustainability: Challenges, Opportunities, and a Research Agenda,” International Journal of Information Management, vol. 53, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] David Rodríguez-Rodríguez, and Javier Martínez-Vega, Protected Areas, Effectiveness of Protected Areas in Conserving Biodiversity, Strategies for Sustainability, Springer, Cham, pp. 21-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Joel Serey et al., “Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and their Role in Engineering Research,” Symmetry, vol. 15, no. 2, pp. 1-29, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Vinamra Bhushan Sharma et al., “Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring,” Metals, vol. 11, no. 10, pp. 1-48, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] M. Tholkapiyan et al., “Examining the Impacts of Climate Variability on Agricultural Phenology: A Comprehensive Approach Integrating Geoinformatics, Satellite Agrometeorology, and Artificial Intelligence,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 6s, pp. 592-598, 2023.
[Google Scholar] [Publisher Link]
[12] Ricardo Vinuesa et al., “The Role of Artificial Intelligence in Achieving the Sustainable Development Goals,” Nature Communications, vol. 11, pp. 1-10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Yongjun Xu et al., “Artificial Intelligence: A Powerful Paradigm for Scientific Research,” Innovation, vol. 2, no. 4, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Manzhu Yu, Chaowei Yang, and Yun Li, “Big Data in Natural Disaster Management: A Review,” Geosciences, vol. 8, no. 5, pp. 1-26, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Bruna Paolinelli Reis et al., “Forest Restoration Monitoring Through Digital Processing of High Resolution Images,” Ecological Engineering, vol. 127, pp. 178-186, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Reham Gharbia, “Deep Learning for Automatic Extraction of Water Bodies Using Satellite Imagery,” Journal of the Indian Society of Remote Sensing, vol. 51, no. 7, pp. 1511-1521, 2023.
[CrossRef] [Google Scholar] [Publisher Link]