Using Artificial Neural Networks for Digital Soil Mapping – a comparison of MLP and SOM approaches

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Portuguese soil map coverage remains incomplete, while the existing cartography has some shortcomings. Artificial Neural Networks (ANN) are advanced computer-based techniques which are being used for Digital Soil Mapping (DSM). These techniques allow mapping soil classes in a cheaper, more consistent and flexible way, using surrogate landscape data. This work compares the performance of two ANN approaches, Multi-layer Perceptron (MLP) and Self-Organizing Map (SOM), for DSM. The tests were carried out in IDRISI Taiga for three catchments in Portugal and one in Spain, using different sampling designs to obtain the training sets. Results reveal that best ANN performance is obtained with a MLP model rather than a SOM model, independently of data transformation and sampling method. However, MLP is also the most sensitive method to the data used to develop the models. Keywords: Digital Soil Mapping, MLP, SOM, IDRISI Taiga, AutoMAPticS, Portugal.
Original languageUnknown
Title of host publicationProceedings of the 16th AGILE
Pagesonline
Publication statusPublished - 1 Jan 2013
EventConference on Geographic Information Science. Leuven, Belgium, May 14-17, 2013 -
Duration: 1 Jan 2013 → …

Conference

ConferenceConference on Geographic Information Science. Leuven, Belgium, May 14-17, 2013
Period1/01/13 → …

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