@conference {12822,
	title = {Recognition of erosion risk areas using neural network technology: an application to the island of Corfu.},
	booktitle = {Pan-European conference {\textquoteleft}Remote Sensing and Spatial Analysis Tools for Erosion Processes{\textquoteright}},
	year = {2006},
	abstract = {<p>There is a wide range of alternative approaches to study erosion processes. In this paper the construction of a model based in the interaction of Geographical Information System (GIS) and Artificial Neural Networks (ANN) is described. The neural model uses supervised competitive learning process. The whole procedure starts with the digitization of the data and the definition of the input variables: such as slope form and gradient, lithology and vegetation - landuse. The neural model<br>transforms the input variables into the erosion risk output variable. Thus, the last stage regarded the creation of an erosion risk zones map. For case study was chosen the island of Corfu (Greece). The island consists of lithologies very vulnerable to erosion and receives considerable amounts of rainfall, especially if compared to the rest of the Greek territory. Finally, the whole model was tested and the proper function of the model was confirmed by field data observations.</p>},
	author = {Gournellos, Th. and Vassilopoulos, A. and Evelpidou, N.}
}
