Neural Network-Based Spatial Modeling of Natural Phenomena and Events

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. The statistic modeling results can be developed and displayed in a user-friendly way within the Esri ArcGIS environment. The complete workflow is documented by the software. This chapter presents five case studies to illustrate the current possibilities and limitations of spatial predictions with the use of artificial neural networks, which describe influencing factors and the selection of known events of the phenomenon to be modeled. These applications include: (1) the prognosis of soil erosion patterns, (2) the country-wide prediction of mineral resources, (3) the vulnerability analysis for forest pests, (4) the spatial distribution of bird species, and (5) the spatial prediction of manganese nodules on the sea bottom.