School of Engineering, UBC-Okanagan, Kelowna, BC, Canada.
Environ Monit Assess. 2013 May;185(5):4171-82. doi: 10.1007/s10661-012-2859-7. Epub 2012 Sep 15.
Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.
矿山酸性排水（AMD）是一个全球性问题，可能对人类健康和环境产生严重影响。实验室和现场测试常用于预测 AMD，但由于多种原因，其形成在不同地点存在差异，因此这具有挑战性。此外，这些测试通常在短时间内进行小规模测试。因此，将这些结果推断到矿山的大规模环境中会给决策者带来巨大的不确定性。本研究提出了机器学习技术，以利用矿山现场的历史监测数据来开发预测 AMD 质量的模型。本研究中探索的机器学习技术包括人工神经网络（ANN）、带有多项式（SVM-Poly）和径向基函数（SVM-RBF）核的支持向量机、模型树（M5P）和 K-最近邻（K-NN）。确定了影响排水动态的理化参数输入变量，并用于开发预测铜浓度的模型。对于这些选定的技术，基于不同的统计指标评估了预测准确性和不确定性。结果表明，SVM-Poly 表现最好，其次是 SVM-RBF、ANN、M5P 和 KNN 技术。总体而言，本研究表明，机器学习技术是预测 AMD 质量的有前途的工具。
