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

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.

[1]  Laura Uusitalo,et al.  Advantages and challenges of Bayesian networks in environmental modelling , 2007 .

[2]  V. Litvinenko.  Digital Economy as a Factor in the Technological Development of the Mineral Sector , 2019, Natural Resources Research.

[3]  Eduardo Moreno-Jiménez,et al.  Screening risk assessment tools for assessing the environmental impact in an abandoned pyritic mine in Spain. , 2011, The Science of the total environment.

[4]  M. Newman,et al.  Statistical mechanics of networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Jian-guo Li,et al.  Intelligent Mining Technology for an Underground Metal Mine Based on Unmanned Equipment , 2018, Engineering.

[6]  Olga Sidorenko,et al.  Rethinking the concept of small-scale mining for technologically advanced raw materials production , 2020 .

[7]  Rafael Rumí,et al.  Bayesian networks in environmental modelling , 2011, Environ. Model. Softw..

[8]  Eduardo Giráldez,et al.  Understanding complex blasting operations: A structural equation model combining Bayesian networks and latent class clustering , 2019, Reliab. Eng. Syst. Saf..

[9]  L. Salvati,et al.  Land quality, sustainable development and environmental degradation in agricultural districts: A computational approach based on entropy indexes , 2017 .

[10]  Saleem H Ali,et al.  Mining and biodiversity: key issues and research needs in conservation science , 2018, Proceedings of the Royal Society B.

[11]  A. Young,et al.  A Review of Digital Transformation in Mining , 2019, Mining, Metallurgy & Exploration.

[12]  Ateekh Ur Rehman,et al.  An Approach to Assess Sustainable Supply Chain Agility for a Manufacturing Organization , 2021, Sustainability.

[13]  L. Mancini,et al.  Social impact assessment in the mining sector: Review and comparison of indicators frameworks , 2018, Resources Policy.

[14]  Chong-chong Qi.  Big data management in the mining industry , 2020, International Journal of Minerals, Metallurgy and Materials.

[15]  Judea Pearl,et al.  Comment on ‘Causal inference, probability theory, and graphical insights’ by Stuart G. Baker , 2013, Statistics in medicine.

[16]  Teresa Rivas,et al.  Use of machine learning techniques to analyse the risk associated with mine sludge deposits , 2011, Math. Comput. Model..

[17]  V. Cárdenes,et al.  Roofing Slate Industry in Spain: History, Geology, and Geoheritage , 2019, Geoheritage.

[18]  M. Müller,et al.  Espacios Naturales en Galicia; un análisis diacrónico de las distintas categorías de protección y de la eficiencia de estas para afrontar el reto de la pérdida de Biodiversidad , 2020 .

[19]  Prakash P. Shenoy,et al.  A causal mapping approach to constructing Bayesian networks , 2004, Decis. Support Syst..

[20]  Georgios Kostopoulos,et al.  Implementing AutoML in Educational Data Mining for Prediction Tasks , 2019, Applied Sciences.

[21]  M. Schmid.  Challenges to the European automotive industry in securing critical raw materials for electric mobility: the case of rare earths , 2020, Mineralogical Magazine.

[22]  Dmitri Krioukov,et al.  Classical information theory of networks , 2019, Journal of Physics: Complexity.

[23]  Manan Sarupria,et al.  Environmental impact assessment studies for mining area in Goa, India, using the new approach , 2018, Environmental Monitoring and Assessment.

[24]  A. Zubarev,et al.  Management of Complex Socio-Economic Systems on the Example of the Mining Industry , 2021 .

[25]  Nevil. Quinn,et al.  Toward an analytical framework for understanding complex social- ecological systems when conducting environmental impact assessments in South Africa , 2015 .

[26]  G. Nhamo,et al.  Effectiveness of Environmental Impact Assessment follow-up as a tool for environmental management: lessons and insights from platinum mines along the Great Dyke of Zimbabwe , 2016, Environmental Earth Sciences.

[27]  F. G. Bastante,et al.  Design and planning for slate mining using optimisation algorithms , 2004 .

[28]  David Maxwell Chickering,et al.  Learning Bayesian Networks is , 1994 .

[29]  Callie W. Babbitt,et al.  The role of design in circular economy solutions for critical materials , 2021 .

[30]  C. Cannaos,et al.  A methodological approach on the procedural effectiveness of EIA: the case of Sardinia , 2019, City, Territory and Architecture.

[31]  Ajoy K. Ghose.  Technology vision 2050 for sustainable mining , 2009 .

[32]  Adam Smoliński,et al.  Environmental impact and damage categories caused by air pollution emissions from mining and quarrying sectors of European countries , 2017 .

[33]  Ramón Argüelles-Fraga,et al.  Room Design for Underground Slate Workings: Analysis of Safety Factors for Keyblocks , 2016, Rock Mechanics and Rock Engineering.

[34]  Sanjoy Kumar Paul,et al.  A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19 , 2020, International Journal of Physical Distribution & Logistics Management.