AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling

Gerassis, Saki; Giráldez, Eduardo; Pazo-Rodríguez, María; Saavedra, Ángeles; Taboada, Javier

doi:10.3390/app11177914

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AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling

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Research Group GESSMin, Department of Natural Resources and Environmental Engineering, University of Vigo, 36310 Pontevedra, Spain

Research Group GESSMin, Department of Statistics and Operational Research, University of Vigo, 36310 Pontevedra, Spain

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Academic Editor: Pietro Picuno

Appl. Sci. 2021, 11(17), 7914; https://doi.org/10.3390/app11177914

Received: 1 July 2021 / Revised: 7 August 2021 / Accepted: 18 August 2021 / Published: 27 August 2021

(This article belongs to the Special Issue AI for Sustainability and Innovation)

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Abstract

Mining engineers and environmental experts around the world still identify and evaluate environmental risks associated with mining activities using field-based, basic qualitative methods The main objective is to introduce an innovative AI-based approach for the construction of environmental impact assessment (EIA) indexes that statistically reflects and takes into account the relationships between the different environmental factors, finding relevant patterns in the data and minimizing the influence of human bias. For that, an AutoML process developed with Bayesian networks is applied to the construction of an interactive EIA index tool capable of assessing dynamically the potential environmental impacts of a slate mine in Galicia (Spain) surrounded by the Natura 2000 Network. The results obtained show the moderate environmental impact of the whole exploitation; however, the strong need to protect the environmental factors related to surface and subsurface runoff, species or soil degradation was identified, for which the information theory results point to a weight between 6 and 12 times greater than not influential variables. View Full-Text

Keywords: artificial intelligence; AutoML; Bayesian networks; sustainable mining; decision making; complex networks

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Gerassis, S.; Giráldez, E.; Pazo-Rodríguez, M.; Saavedra, Á.; Taboada, J. AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling. Appl. Sci. 2021, 11, 7914. https://doi.org/10.3390/app11177914

AMA Style

Gerassis S, Giráldez E, Pazo-Rodríguez M, Saavedra Á, Taboada J. AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling. Applied Sciences. 2021; 11(17):7914. https://doi.org/10.3390/app11177914

Chicago/Turabian Style

Gerassis, Saki, Eduardo Giráldez, María Pazo-Rodríguez, Ángeles Saavedra, and Javier Taboada. 2021. "AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling" Applied Sciences 11, no. 17: 7914. https://doi.org/10.3390/app11177914

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AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling

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