Abstract
Soil lead pollution does great harm to the environment. The relation between soil lead and environmental covariates, however, is complicated. A fuzzy logic approach with expert knowledge has proven to be successful in mapping spatial variation of soil lead. The reasons are the limited availability of expert knowledge and the ignorance of soil type and land use of the non-soil area when predicting soil lead in current fuzzy clustering methodology. This paper incorporates the soil type map, the land use map and the DEM data to construct a soil\'96\'6 landscape model for soil lead prediction. It compares a fuzzy C-means classifier that includes expert judgment with conditional regressive model. Prediction efficiency was evaluated in the Three Gorges area of China using the root mean square error (RMSE) and the agreement coefficient (AC) of predictions at validation points. The result indicates that the soil-landscape model constructed by the fuzzy membership functions with fuzzy c-means method and the conventional soil map were able to produce good quality soil lead spatial information.
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