Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

被引:35
作者
Betrie, Getnet D. [1 ]
Tesfamariam, Solomon [1 ]
Morin, Kevin A. [2 ]
Sadiq, Rehan [1 ]
机构
[1] UBC Okanagan, Sch Engn, Kelowna, BC, Canada
[2] Minesite Drainage Assessment Grp, Vancouver, BC, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Acid mine drainage; Acid rock drainage; Machine learning; Artificial neural network; Support vector machine; Model tree; K-nearest neighbors;
D O I
10.1007/s10661-012-2859-7
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:4171 / 4182
页数:12
相关论文
共 34 条
[1]  
Allison JerryD., 1991, MINTEQA2PRODEFA2 GEO
[2]  
[Anonymous], 2007, LEARN DATA CONCEPTS, DOI DOI 10.1002/9780470140529.CH4.[38]L
[3]  
[Anonymous], 1997, PROC 9 EUR C MACH LE
[4]  
[Anonymous], 1992, 5 AUSTR JOINT C ART
[5]  
[Anonymous], 1997, MACHINE LEARNING, MCGRAW-HILL SCIENCE/ENGINEERING/MATH
[6]   Developing a framework for sustainable development indicators for the mining and minerals industry [J].
Azapagic, A .
JOURNAL OF CLEANER PRODUCTION, 2004, 12 (06) :639-662
[7]  
Bishop CM., 1995, NEURAL NETWORKS PATT
[8]  
Bouckaert R. R., 2010, WEKA MANUAL VERSION, V327
[9]   Computational intelligence in earth sciences and environmental applications: Issues and challenges [J].
Cherkassky, V. ;
Krasnopolsky, V. ;
Solomatine, D. P. ;
Valdes, J. .
NEURAL NETWORKS, 2006, 19 (02) :113-121
[10]  
Gray NF, 1996, ENVIRON GEOL, V27, P358