Abstract:

 Considering the fact that a very limited amount of data of soil pollution are

available and the demand of the urban development, the authors try to develop a model of

soil pollution using impact factors that affect the quality of soil environment for the

assessment and prediction of the soil pollution. The study employs the support vector

machine (SVM) method, a fairly new pattern recognition tool, the advantages of which are the

good adaptation to the case of limited samples, the effective generalization ability and the

higher accuracy. The 9 impact factors including GDP, the amount of waste water, gas and

solid waste, population, rainfall, vegetation, etc. are chosen as the input variables of the

model and the percentages of the concentrations of Cu, Pb, Zn, Cd, Co, Ni, Cr, and Mn in

polluted soil are taken as the output variables. The SVM model was trained and tested on 879

soil samples collected in 1986 and 2003, and 51 impact factor samples spanning 17 years. For

optimizing and approximating the implicit performance function, we employed a Gauss kernel

function and calculated the value kernel (γ), loss function insensibility (ε) and punish

function parameters (C) by genetic algorithm methods; the corresponding values are 1.021,

0.000416 and 1012, respectively. Eventually we thus obtained the SVMbased explicit

performance functions of soil pollution with impact factors, and the model has shown its

effectiveness.

Key words:

Key words: soil pollution; impact factors; assessment model; support vector machine (SVM)