| Résumé | The Equity Silver mine site, British Columbia, Canada, is known for its management and treatment of acid rock drainage. The accurate prediction of full-scale seepage flow rate in field conditions is critical for mine site management, flood control, and contaminant treatment strategies. In this study, a machine learning methodology based on artificial neural network (ANN) is proposed to correlate measured seepage flow rate with the weather monitoring data collected from the field of Equity Silver mine. Comparing with traditional numerical and water balance approaches, the ANN approach generally requires much fewer investigations on hydrogeological mechanisms or definitions of parameters and equations. The measured seepage flow rates from the Bessemer Dump and the weather monitoring data during 1998–2017 are used for training the proposed ANN. The calculated results from the trained ANN show good agreement with the measurements. In addition, the proposed ANN approach is further refined by including time tags of the monitoring data in the input layer, which enable the ANN to investigate the effects of long-term weather trends, climate change, and dynamics inside waste rock piles on seepage flow. The prediction results for the following year (2017–2018) from the refined ANN and the original ANN are compared, which shows the improvement of the refined ANN approach. |
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