Predicting the distribution of discrete spatial events using artificial neural networks

Skabar, Andrew 2003, Predicting the distribution of discrete spatial events using artificial neural networks, in AI 2003 : Advances in Artificial Intelligence : Proceedings of the 16th Australian Conference on AI, Springer, [Perth, W.A.], pp. 567-577.

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Title Predicting the distribution of discrete spatial events using artificial neural networks
Author(s) Skabar, Andrew
Conference name Advances in Artificial Intelligence Australian Conference (16th : 2003 : Perth, W.A.)
Conference location Perth, W.A.
Conference dates 3-5 Dec. 2003
Title of proceedings AI 2003 : Advances in Artificial Intelligence : Proceedings of the 16th Australian Conference on AI
ISBN ***DO NOT USE*** 3-540-20646-9
Editor(s) Gedeon, T.
Chun Che Fung, L.
Publication date 2003
Start page 567
End page 577
Publisher Springer
Place of publication [Perth, W.A.]
Summary 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.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005060

Document type: Conference Paper
Collection: School of Information Technology
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