Artificial neural network for acid sulfate soil mapping: Application to the Sirppujoki River catchment area, south-western Finland

A1 Originalartikel i en vetenskaplig tidskrift (referentgranskad)


Interna författare/redaktörer


Publikationens författare: Amélie Beucher, Peter Österholm, Annu Martinkauppi, Peter Eden, Sören Fröjdö
Förläggare: ELSEVIER SCIENCE BV
Publiceringsår: 2013
Tidskrift: Journal of Geochemical Exploration
Tidskriftsakronym: J GEOCHEM EXPLOR
Volym: 125
Artikelns första sida, sidnummer: 46
Artikelns sista sida, sidnummer: 55
Antal sidor: 10
ISSN: 0375-6742


Abstrakt

In Finland, acid sulfate (AS) soils constitute a major environmental issue. These soils leach considerable amounts of metals into watercourses, causing severe ecological damage. As small hot spot areas affect large coastal waters, mapping constitutes an essential step in the management of AS soil environmental risks (i.e. to target strategic places where to put mitigation). The primordial aim of this study was to evaluate the predictive classification abilities of an Artificial Neural Network (ANN) for AS soil mapping. The Sirppujoki River catchment (460 km(2)) located in south-western Finland was selected as study area. An ANN called Radial Basis Functional Link Nets (RBFLN) was applied in order to create probability maps for AS soil occurrences in the study area. This method required the use of aerogeophysical, quaternary geology and elevation data, as well as known AS soil and non-AS soil sites. Applying the RBFLN method, we generated different probability maps. For the most accurate probability map, the combined very high and high probability areas covered 23% of the study area and contained 94% of the validation points corresponding to AS soil occurrences. The combined low and very low probability areas occupied the remaining 77% of the study area and contained all the validation points corresponding to non-AS soil sites. These results being consistent with previous studies and verified by expert assessment, the RBFLN method demonstrated reliable and robust predictive classification abilities for AS soil mapping in the study area. This spatial modelling technique allows the creation of valid and comparable maps, and represents a powerful development within the AS soil mapping process, making it faster and more efficient. Consequently, we recommend the RBFLN modelling, finalized by an expert assessment, for AS soil mapping. (C) 2012 Elsevier B.V. All rights reserved.


Nyckelord

Acid sulfate soils, Artificial neural network, Probability map, Radial basis functional link net

Senast uppdaterad 2019-20-11 vid 04:15