RT Journal Article
T1 AI approaches to environmental impact assessments (EIAs) in the mining and metals sector using AutoML and Bayesian modeling
A1 Gerassis Davite, Saki
A1 Giráldez Pérez, Eduardo
A1 Pazo Rodríguez, María
A1 Saavedra González, Maria Angeles
A1 Taboada Castro, Javier
K1 1203.04 Inteligencia Artificial
K1 5312.09 Minería
K1 3308 Ingeniería y Tecnología del Medio Ambiente
AB 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.
PB Applied Sciences
SN 20763417
YR 2021
FD 2021-08-27
LK http://hdl.handle.net/11093/2496
UL http://hdl.handle.net/11093/2496
LA eng
NO Applied Sciences, 11(17): 7914 (2021)
DS Investigo
RD 20-abr-2025