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Spatial Modeling of Natural Phenomena and Events with Artificial Neural Networks and GIS

Author

Listed:
  • Silke Noack

    (Beak Consultants GmbH, Germany)

  • Andreas Barth

    (Beak Consultants GmbH, Germany)

  • Alexey Irkhin

    (Beak Consultants GmbH, Germany)

  • Evelyn Bennewitz

    (Beak Consultants GmbH, Germany)

  • Frank Schmidt

    (Beak Consultants GmbH, Germany)

Abstract

Artificial neural networks (ANN) are used for statistical modeling of spatial events in geosciences. The advantage of this method is the ability of neural networks to represent complex interrelations and to be “able to learn” from known (spatial) events. The software advangeo® was developed to enable GIS users to apply neural network methods on raster geodata. This statistic modeling can be displayed in a user-friendly way within the ESRI ArcGIS environment. The complete workflow is documented by the software. This paper presents three pilot studies conducted to illustrate the possibilities of spatial predictions with the use of existing raster datasets, which described influencing factors and the selection of known events of the phenomenon to be modeled. These applications included (1) the prognosis of soil erosion patterns, (2) the prediction of mineral resources, and (3) vulnerability analysis for forest pests.

Suggested Citation

  • Silke Noack & Andreas Barth & Alexey Irkhin & Evelyn Bennewitz & Frank Schmidt, 2012. "Spatial Modeling of Natural Phenomena and Events with Artificial Neural Networks and GIS," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 3(1), pages 1-20, January.
    • Handle: RePEc:igg:jagr00:v:3:y:2012:i:1:p:1-20
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