The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.
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.
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.
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.
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