Preprint / Version 1

Implications of Artificial Intelligence in Environmental Engineering

##article.authors##

  • Neha Bachu Greenhill School

DOI:

https://doi.org/10.58445/rars.585

Keywords:

Artificial Intelligence, Environmental Engineering, Ethics, Climate Change, Environmental Racism

Abstract

As environmental destruction progresses at an alarming rate, the threat of ecological catastrophes and the potential damages that communities around the world face demand that solutions be implemented immediately. Environmental engineers are at the forefront of grappling with the rippling effects of climate change, and to keep up with these challenges, a novel method of solving environmental crises must come to play: artificial intelligence. Artificial intelligence (AI) models can make educated predictions, identify significant patterns, and analyze large amounts of data, to help optimize and improve current environmental engineering processes for the future. This paper surveys different applications of AI that have been used by environmental engineers to enhance current technologies and practices in disciplines ranging from the petroleum industry to carbon capture.  Additionally, we consider the ethical implications and unintended consequences that can result from an increased use of AI. Discussing the utilization of artificial intelligence in environmental engineering can ultimately help develop more effective methods for combating current environmental challenges, and further examination on the ethical implications of this usage can help ensure environmental justice for all.

References

Awoleke, O. O., & Lane, R. H. (2011). Analysis of Data From the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques. SPE Reservoir Evaluation & Engineering, 14(05), 544–556. https://doi.org/10.2118/127919-PA

Basalyga, J. N., Barajas, C. A., Gobbert, M. K., & Wang, J. (2021). Performance Benchmarking of Parallel Hyperparameter Tuning for Deep Learning Based Tornado Predictions. Big Data Research, 25, 100212. https://doi.org/10.1016/j.bdr.2021.100212

Carter, L., Liu, D., & Cantrell, C. (2020). Exploring the Intersection of the Digital Divide and Artificial Intelligence: A Hermeneutic Literature Review. AIS Transactions on Human-Computer Interaction, 12(4), 253–275. https://doi.org/10.17705/1thci.00138

Crawford, K., & Joler, V. (2018). Anatomy of an AI System: The Amazon Echo As An Anatomical Map of Human Labor, Data and Planetary Resources. AI Now Institute and Share Lab. https://anatomyof.ai/

Demortain, D. (2020). The Science of Bureaucracy. The MIT Press. https://doi.org/10.7551/mitpress/12248.001.0001

Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., McPhearson, T., Jimenez, D., King, B., Larcey, P., & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, 101741. https://doi.org/10.1016/j.techsoc.2021.101741

Hino, M., Benami, E., & Brooks, N. (2018). Machine learning for environmental monitoring. Nature Sustainability, 1(10), 583–588. https://doi.org/10.1038/s41893-018-0142-9

Huang, S., Li, W., Wu, J., Zhang, H., & Luo, Y. (2020). A study of the mechanism of nonuniform production rate in shale gas based on nonradioactive gas tracer technology. Energy Science & Engineering, 8(7), 2648–2658. https://doi.org/10.1002/ese3.691

Jackson, R. B., Vengosh, A., Carey, J. W., Davies, R. J., Darrah, T. H., O’Sullivan, F., & Pétron, G. (2014). The Environmental Costs and Benefits of Fracking. Annual Review of Environment and Resources, 39(1), 327–362. https://doi.org/10.1146/annurev-environ-031113-144051

Keshavarzi, R. ., & Jahanbakhshi, R. . (2013, January 28). Real-Time Prediction of Complex Hydraulic Fracture Behaviour in Unconventional Naturally Fractured Reservoirs. All Days. https://doi.org/10.2118/163950-MS

Krzywanski, J. (2022). Advanced AI Applications in Energy and Environmental Engineering Systems. In Energies (Vol. 15, Issue 15). MDPI. https://doi.org/10.3390/en15155621

Lemieux, J.-M. (2011). Review: The potential impact of underground geological storage of carbon dioxide in deep saline aquifers on shallow groundwater resources. Hydrogeology Journal, 19(4), 757–778. https://doi.org/10.1007/s10040-011-0715-4

Li, C. (2020). Biodiversity assessment based on artificial intelligence and neural network algorithms. Microprocessors and Microsystems, 79, 103321. https://doi.org/10.1016/j.micpro.2020.103321

Manyika, J. (2022). Introductory Notes on AI & Society. Daedalus, 151(2), 5–27. https://www.jstor.org/stable/48662023

McGovern, A., Ebert-Uphoff, I., Gagne, D. J., & Bostrom, A. (2022). Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science. Environmental Data Science, 1, e6. https://doi.org/10.1017/eds.2022.5

Mohai, P. (2018). ENVIRONMENTAL JUSTICE AND THE FLINT WATER CRISIS. Michigan Sociological Review, 32, 1–41. https://www.jstor.org/stable/26528595

Morozov, A. D., Popkov, D. O., Duplyakov, V. M., Mutalova, R. F., Osiptsov, A. A., Vainshtein, A. L., Burnaev, E. V., Shel, E. V., & Paderin, G. V. (2020). Data-driven model for hydraulic fracturing design optimization: focus on building digital database and production forecast. Journal of Petroleum Science and Engineering, 194. https://doi.org/10.1016/j.petrol.2020.107504

Nile, B. K. (2018). Effectiveness of Hydraulic and Hydrologic Parameters in Assessing Storm System Flooding. Advances in Civil Engineering, 2018, 1–17. https://doi.org/10.1155/2018/4639172

Oxford English Dictionary. (n.d.). artificial intelligence. https://www.oed.com/viewdictionaryentry/Entry/271625

Riggins, F., & Dewan, S. (2005). The Digital Divide: Current and Future Research Directions. Journal of the Association for Information Systems, 6(12), 298–337. https://doi.org/10.17705/1jais.00074

Salih, A. L., Hochschule, D., & Ravensburg, B.-W. (2020). Artificial Intelligence in Engineering and Society - Current Trends and Applications. https://doi.org/10.12903/DHBW_RV_FN_03_2020

Sene, K. (2010). Hydrological Forecasting. In Hydrometeorology (pp. 101–140). Springer Netherlands. https://doi.org/10.1007/978-90-481-3403-8_4

Silvestro, D., Goria, S., Sterner, T., & Antonelli, A. (2022). Improving biodiversity protection through artificial intelligence. Nature Sustainability, 5(5), 415–424. https://doi.org/10.1038/s41893-022-00851-6

Sirisena, T. A. J. G., Maskey, S., & Ranasinghe, R. (2020). Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin. Remote Sensing, 12(22), 3768. https://doi.org/10.3390/rs12223768

Steinkruger, D., Markowski, P., & Young, G. (n.d.). An Artificially Intelligent System for the Automated Issuance of Tornado Warnings in Simulated Convective Storms. https://doi.org/10.1175/WAF-D-19

Torres, E. (2016, April 11). Biodiversity loss: An existential risk comparable to climate change. https://thebulletin.org/2016/04/biodiversity-loss-an-existential-risk-comparable-to-climate-change/

Wagenaar, D., Curran, A., Balbi, M., Bhardwaj, A., Soden, R., Hartato, E., Mestav Sarica, G., Ruangpan, L., Molinario, G., & Lallemant, D. (2020). Invited perspectives: How machine learning will change flood risk and impact assessment. Natural Hazards and Earth System Sciences, 20(4), 1149–1161. https://doi.org/10.5194/nhess-20-1149-2020

Wen, G., Li, Z., Azizzadenesheli, K., Anandkumar, A., & Benson, S. M. (2022). U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow. Advances in Water Resources, 163, 104180. https://doi.org/10.1016/j.advwatres.2022.104180

World Health Organization. (n.d.). Floods. WHO. https://www.who.int/health-topics/floods

Zeng, Q., Qing, Z., Zhu, M., Zhang, F., Wang, H., Liu, Y., Shi, Z., & Yu, Q. (2022). Application of Random Forest Algorithm on Tornado Detection. Remote Sensing, 14(19), 4909. https://doi.org/10.3390/rs14194909

Downloads

Posted

2023-10-10