土壤环境质量支持向量机预测模型初探－以湖南长沙、株洲、湘潭地区为例

TL;DRAbstract

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 arc 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 ap

Chat with Paper

AI Agents for this Paper