Optimization of clustering parameters in predictive mapping of soil organic matter

After fuzzy c-means clustering of topographic attributes derived from a digital elevation model (DEM) of the study area, three measures had been used to identify optimal combinations of clustering parameters, i.e. clustering exponent and number of classes, and its corresponding clustering results. Based on the identified optimal clustering results, soil-landscape models were built through multiple linear regressing the relationships between the content of organic matter in soil A layer and clustering memberships. Furthermore, the soil-landscape models were applied into predictive mapping of organic matter in soil A layer over the study area. Results show that, the three measures have different powers in choosing optimal number of clusters while they chose the similar optimal fuzziness exponents, 1.5 and/or near to 1.5, i.e. 1.4 and 1.6. And the predicted maps that were based on the optimal clustering identified by regression r2, show greater deviations from normal than the other two maps. Moreover, comparisons of values on predicted maps and determined in lab indicated that greater accuracies existed in the predictive maps which were based on the fuzzy clustering identified by internal criterion.