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dc.contributor.authorBagheri Bodaghabadi, Mohsen
dc.contributor.authorMartínez Casasnovas, José Antonio
dc.contributor.authorSalehi, Mohammad Hasan
dc.contributor.authorMohammadi, Jahangard
dc.contributor.authorEsfandiarpoor Borujeni, Isa
dc.contributor.authorToomanian, Norair
dc.contributor.authorGandomkar, Amir
dc.date.accessioned2016-01-20T13:38:55Z
dc.date.available2017-08-31T22:13:15Z
dc.date.issued2015
dc.identifier.issn1002-0160
dc.identifier.urihttp://hdl.handle.net/10459.1/49354
dc.description.abstractDetailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.ca_ES
dc.language.isoengca_ES
dc.publisherElsevierca_ES
dc.relation.isformatofVersió postprint del document publicat a https://doi.org/10.1016/S1002-0160(15)30038-2ca_ES
dc.relation.ispartofPedosphere, 2015, vol. 25, núm. 4, p. 580-591ca_ES
dc.rights(c) Elsevier, 2015ca_ES
dc.subjectDigital elevation model attributesca_ES
dc.subjectMultilayer perceptronca_ES
dc.subjectSoil classificationca_ES
dc.subjectSoil-forming factorsca_ES
dc.subject.otherXarxes neuronals (Informàtica)ca_ES
dc.subject.otherSòlsca_ES
dc.subject.otherAltituds -- Mesuramentca_ES
dc.titleDigital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributesca_ES
dc.typearticleca_ES
dc.identifier.idgrec020803
dc.type.versionacceptedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1016/S1002-0160(15)30038-2


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