Premium
A fuzzy allocation scheme for the Australian Great Soil Groups Classification system
Author(s) -
MAZAHERI S.A.,
KOPPI A.J.,
McBRATNEY A.B.
Publication year - 1995
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/j.1365-2389.1995.tb01356.x
Subject(s) - centroid , fuzzy logic , unified soil classification system , soil water , classification scheme , statistics , data mining , component (thermodynamics) , mathematics , sample (material) , soil science , soil classification , computer science , geology , artificial intelligence , machine learning , chemistry , physics , chromatography , thermodynamics
Summary Conventional methods of soil classification are based largely on hierarchies and treat soil attribute value ranges as exactly specifiable quantities. Misclassification may result from ignoring the continuous complex nature of soil variation, and the inability to sample and measure every aspect of soil. The Australian Great Soil Groups (GSG) system of classification is essentially a fuzzy classification, and for the most part central concepts have not been defined explicitly. Centroids were generated, together with fuzzy group membership, using data from the 147 soil profile descriptions in The Handbook of Australian Soils . Some of the GSG, such as Siliceous Sands and Red and Brown Hardpan Soils, were divided into their component parts for better and easier quantification and allocation. The centroids of GSG were examined, and the method of fuzzy κ‐means was then used to allocate unknown profiles to the GSG. The results show that the system is intuitively reasonable.