Use of machine learning techniques to analyse the risk associated with mine sludge deposits

Sludge deposits resulting from mine extraction activities and accumulating in the proximity of production centres have an important potential impact on their surroundings. This potential impact needs to be evaluated by quantifying the risk of an accident on the basis of a joint study of factors affecting the probability of occurrence, environmental, populational and infrastructural vulnerability factors and intrinsic and extrinsic risk factors. The problem is non-linear, and this fact, combined with the high number of risk conditioning variables, justifies using machine learning techniques to estimate risk. A comparison of results for supervised versus non-supervised learning techniques confirms that the former adapts better to the problem than the latter.

[1]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[2]  Javier Taboada,et al.  Assessing the viability of underground slate mining by combining an expert system with a GIS , 2006 .

[3]  Stéphane Canu,et al.  Environmental data mining and modeling based on machine learning algorithms and geostatistics , 2004, Environ. Model. Softw..

[4]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[5]  Francisco José Colomer Mendoza,et al.  Environmental risk index: A tool to assess the safety of dams for leachate , 2009 .

[6]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[7]  W. González-Manteiga,et al.  Support vector machines and gradient boosting for graphical estimation of a slate deposit , 2004 .

[8]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Javier Taboada,et al.  Machine learning techniques applied to the construction of a new geomechanical quality index , 2011, Int. J. Comput. Math..

[11]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[12]  Xiao Ming,et al.  Coal Enterprises Merger and Acquisition Risk Prediction Based on Support Vector Machine , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[13]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[14]  Allan Pinkus,et al.  Approximation theory of the MLP model in neural networks , 1999, Acta Numerica.

[15]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[16]  Antonio Gallardo Izquierdo,et al.  Design of a model to assess the environmental risk of leachate dams , 2008 .

[17]  C. Arregui,et al.  Assessing the risk of pesticide environmental impact in several Argentinian cropping systems with a fuzzy expert indicator. , 2010, Pest management science.

[18]  E Benfenati,et al.  Assessing the environmental risks associated with contaminated sites , 2011 .

[19]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..