National Research Council of Canada. Energy, Mining and Environment
machine learning; long short-term memory; full-scale waste rock piles; drainage chemistry; historical monitoring data
In this paper, a machine learning algorithm based on artificial neural network architecture investigates the correlation between drainage chemistries in seepage water and ambient weather conditions around waste rock piles. The proposed neural network consists of a long short-term memory unit and a fully connected neural network which uses sequenced input to consider current and previous weather impact on the drainage chemistries. A 20-year (1998–2017) monitoring database obtained from the full-scale waste rock pile of the Equity Silver mine in BC, Canada is used for validating the proposed approach. The neural network is trained based on total precipitation and mean temperature as input and the acidity as output. The results indicate that the calculated acidity from the trained neural network matches with that measured in the field well. In addition, the accuracy of calculated acidity can be further increased by adding a time tag of acidity measurement date into the input layer. This refined approach can capture the long-term evolution and dynamics of hydrogeochemical and biochemical properties inside the waste rock piles.
Journal of Contaminant Hydrology239, 103793: 103793–.