Spatial predictive mapping using artificial neural networks
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 parameter...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2014-11-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2/79/2014/isprsarchives-XL-2-79-2014.pdf |
| _version_ | 1828524323197943808 |
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| author | S. Noack A. Knobloch S. H. Etzold A. Barth E. Kallmeier |
| author_facet | S. Noack A. Knobloch S. H. Etzold A. Barth E. Kallmeier |
| author_sort | S. Noack |
| collection | DOAJ |
| description | 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.
<br><br>
In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling
approach compared to the exact mathematical modelling.
<br><br>
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. |
| first_indexed | 2024-12-11T20:44:20Z |
| format | Article |
| id | doaj.art-413d6e40de934a5e9f7ff3ce957e14ea |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-11T20:44:20Z |
| publishDate | 2014-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-413d6e40de934a5e9f7ff3ce957e14ea2022-12-22T00:51:24ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342014-11-01XL-2798610.5194/isprsarchives-XL-2-79-2014Spatial predictive mapping using artificial neural networksS. Noack0A. Knobloch1S. H. Etzold2A. Barth3E. Kallmeier4Beak Consultants GmbH, 09599 Freiberg, GermanyBeak Consultants GmbH, 09599 Freiberg, GermanyBeak Consultants GmbH, 09599 Freiberg, GermanyBeak Consultants GmbH, 09599 Freiberg, GermanyBeak Consultants GmbH, 09599 Freiberg, GermanyThe 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. <br><br> In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. <br><br> 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.http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2/79/2014/isprsarchives-XL-2-79-2014.pdf |
| spellingShingle | S. Noack A. Knobloch S. H. Etzold A. Barth E. Kallmeier Spatial predictive mapping using artificial neural networks The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Spatial predictive mapping using artificial neural networks |
| title_full | Spatial predictive mapping using artificial neural networks |
| title_fullStr | Spatial predictive mapping using artificial neural networks |
| title_full_unstemmed | Spatial predictive mapping using artificial neural networks |
| title_short | Spatial predictive mapping using artificial neural networks |
| title_sort | spatial predictive mapping using artificial neural networks |
| url | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2/79/2014/isprsarchives-XL-2-79-2014.pdf |
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