Abstract
Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.
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References
Civco, D.L.: Artificial neural networks for land-cover classification. International Journal of Geographical Information Systems 7(2), 173–186 (1990)
Miller, D.M., Kaminsky, E.J., Rana, S.: Neural network classification of remote-sensing data. Computers and Geosciences 21(2), 377–386 (1995)
Mohanty, K.K., Majumdar, T.J.: An artificial neural network (ANN) based software package for classification of remotely sensed data. Computers & Geosciences 22(1), 81–87 (1996)
Everitt, B.: Cluster Analysis, London, Heinemann (1980)
Agterberg, F.P.: Geomathematics: Mathematical Background and Geo-Science Applications. Elsevier Scientific Publishing Company, Amsterdam (1974)
Bonham-Carter, G.F.: Geographic Information Systems for Geoscientists: Modelling with GIS. Elsevier Science Ltd, U.K (1994)
Duda, R.O., Hart, P.E.: Pattern Recognition and Scene Analysis. John Wiley & Sons, New York (1973)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Baum, E.B., Wilczek, F.: Supervised learning of probability distributions by neural networks. In: Anderson, D.Z. (ed.) Neural Information Processing Systems, American Inst. of Physics, New York, pp. 52–61 (1988)
Schumacher, M., Rossner, R., Vach, W.: Neural networks and logistic regression: part 1. Computational Statistics & Data Analysis 21, 661–682 (1996)
Cochrane, G.W., Quick, G.W., Spencer-Jones, D. (eds.): Introducing Victorian Geology, 2nd edn, Geological Society of Australia Incorporated (Victorian Division) Melbourne, Australia (1995)
Clark, I., Cook, B. (eds.): Victorian Geology Excursion Guide. Australian Academy of Science, Canberra (1988)
Willman, C.E., Goldfield, C.: Castlemaine-Chewton, Fryers Creek 1: 10 000 Maps Geological Report, Geological Survey Report 106, Energy and Minerals Victoria (1995)
Skabar, A.: Inductive Learning Techniques for Mineral Potential Mapping, PhD Thesis, School of Electrical and Electronic Systems Engineering, Queensland University of Technology, Australia (2000)
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© 2003 Springer-Verlag Berlin Heidelberg
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Skabar, A. (2003). Predicting the Distribution of Discrete Spatial Events Using Artificial Neural Networks. In: Gedeon, T.(.D., Fung, L.C.C. (eds) AI 2003: Advances in Artificial Intelligence. AI 2003. Lecture Notes in Computer Science(), vol 2903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24581-0_48
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DOI: https://doi.org/10.1007/978-3-540-24581-0_48
Publisher Name: Springer, Berlin, Heidelberg
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