Harnessing Artificial Intelligence for Sustainable Environmental Solutions: A Deep Learning Approach


  • Mustafa Abdulhadi Ben Hmaida Higter Institute for Sciences &Technology-Misrata


Artificial Intelligence, Environmental Sustainability, Deep Learning, Pollution Monitoring


This paper explores the profound impact of Artificial Intelligence (AI) and Deep Learning on environmental sustainability. It investigates various environmental domains, including pollution monitoring, resource management, climate change predictions, and biodiversity conservation, highlighting AI's transformative potential. AI's ability to uncover hidden patterns and trends in environmental data is discussed, particularly in climate change predictions and biodiversity conservation. The implications of AI in environmental management are profound, enabling timely interventions in pollution control, resource preservation, and climate change mitigation, aligning with Sustainable Development Goals (SDGs). However, challenges such as data quality and ethical concerns are noted. Future research directions emphasize refining AI applications to address these challenges and further enhance environmental sustainability


Alahakoon, D., Nawaratne, R., Xu, Y., De Silva, D., Sivarajah, U., & Gupta, B. (2020). Information Systems Frontiers.

Alahakoon, D., Nawaratne, R., Xu, Y., De Silva, D., Sivarajah, U., & Gupta, B. (2020). Self-building artificial intelligence and machine learning to empower big data analytics in smart cities. Information Systems Frontiers, 1-20.

Andronie, M., Lăzăroiu, G., Iatagan, M., Uță, C., Ștefănescu, R., & Cocoșatu, M. (2021). Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10(20), 2497.

Arfanuzzaman, M. (2021). Harnessing artificial intelligence and big data for SDGs and prosperous urban future in South Asia. Environmental and sustainability indicators, 11, 100127.

Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Environmental Science and Ecotechnology, 19.

Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, 100330.

Egarter Vigl, L., Marsoner, T., Giombini, V., Pecher, C., Simion, H., Stemle, E., ... & Depellegrin, D. (2021). Harnessing artificial intelligence technology and social media data to support Cultural Ecosystem Service assessments. People and Nature, 3(3), 673-685.

Fan, Z., Yan, Z., & Wen, S. (2023). Deep learning and artificial intelligence in sustainability: a review of SDGs, renewable energy, and environmental health. Sustainability, 15(18), 13493.

Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., ... & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 101741.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 134120.

Kler, R., Elkady, G., Rane, K., Singh, A., Hossain, M. S., Malhotra, D., ... & Bhatia, K. K. (2022). Machine learning and artificial intelligence in the food industry: a sustainable approach. Journal of Food Quality, 2022, 1-9.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Leonard, K. C., Hasan, F., Sneddon, H. F., & You, F. (2021). Can artificial intelligence and machine learning be used to accelerate sustainable chemistry and engineering?. ACS Sustainable Chemistry & Engineering, 9(18), 6126-6129.

Malik, H., Chaudhary, G., & Srivastava, S. (2022). Digital transformation through advances in artificial intelligence and machine learning. Journal of Intelligent & Fuzzy Systems, 42(2), 615-622.

McCoubrey, L. E., Elbadawi, M., Orlu, M., Gaisford, S., & Basit, A. W. (2021). Harnessing machine learning for development of microbiome therapeutics. Gut Microbes, 13(1), 1872323.

Mhlanga, D. (2022). The role of artificial intelligence and machine learning amid the COVID-19 pandemic: What lessons are we learning on 4IR and the sustainable development goals. International Journal of Environmental Research and Public Health, 19(3), 1879.

Mousavi, R., Raghu, T. S., & Frey, K. (2020). Harnessing artificial intelligence to improve the quality of answers in online question-answering health forums. Journal of Management Information Systems, 37(4), 1073-1098.

Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson Education Limited.

Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., ... & Kiesecker, J. M. (2022). Potential for artificial intelligence (AI) and machine learning (ML) applications in biodiversity conservation, managing forests, and related services in India. Sustainability, 14(12), 7154.

Yigitcanlar, T. (2021). Greening the artificial intelligence for a sustainable planet: An editorial commentary. Sustainability, 13(24), 13508.

Zhou, Z., McCarthy, D. T., & Deletic, A. (2021). Deep learning for environmental prediction and analysis. Journal of Environmental Management, 283, 111949.



How to Cite

Ben Hmaida, M. A. (2024). Harnessing Artificial Intelligence for Sustainable Environmental Solutions: A Deep Learning Approach. Journal of Reproducible Research, 2(2), 153–161. Retrieved from https://journalrrsite.com/index.php/Myjrr/article/view/84