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.

本文研究了一种基于人工神经网络架构的机器学习算法，研究了渗水排水化学特性与waste石桩周围环境天气条件之间的相关性。拟议的神经网络由一个长短期记忆单元和一个完全连接的神经网络组成，该神经网络使用顺序输入来考虑当前和先前的天气对排水化学的影响。从加拿大不列颠哥伦比亚省Equity Silver矿山的满规模废石堆中获得的20年（1998-2017年）监控数据库用于验证所建议的方法。基于总降水量和平均温度作为输入以及酸度作为输出来训练神经网络。结果表明，由训练后的神经网络计算出的酸度与现场井中测得的酸度相匹配。另外，通过将酸度测量日期的时间标签添加到输入层中，可以进一步提高计算出的酸度的精度。这种改进的方法可以捕获the石桩内水文地球化学和生化特性的长期演变和动态。