基于神经网络的土壤重金属预测及生态风险评价

采用单隐层RBF神经网络模型预测土壤重金属Cr、As、Ni、Pb、Zn 5种元素的含量,实测35组数据做为训练数据,另用6组做验证数据,该模型是以利用采样的10组数据预测其后的连续5组数据,输入层的神经元个数是10,输出层是5,隐含层的传递函数为径向基函数radbas,输出层的传递函数为线性函数Purelin,其结果表明:采用RBF神经网络模型预测有较高的精度。通过多元统计分析采样样品与预测样品,研究区域As、Ni、Zn的均值超过了上海市土壤环境背景值,As元素达到高度变异,Pb、Zn、Ni 3种元素达到中度变异。通过因子分析,前2个因子基本包含了全部元素变量的主要信息,第1因子中载荷最高是元素Ni(0.946),第2因子中则为元素As(0.930)。通过潜在生态风险指数评价,研究区域整体呈轻度生态风险水平。采用RBF神经网络模型可以降低采样分析成本,更好的评价区域土壤重金属的生态风险。

PREDICTION AND ECOLOGICAL RISK ASSESSMENT OF HEAVY METALS IN SOIL BASED ON NEURAL NETWORK

The Radial Basis Function (RBF) neural network was used to predict the contents of soil heavy metals of Cr,As,Hg,Ni,Pb,Zn in study area.We selected 35 groups of measured data as training data,with other 6 groups as validation data.The model was constituted continuous data of the 5 groups followed 10 groups of data based on continuous sampling.The number of neurons in the input layer is 10,and the output layer is 5.The transfer function of the hidden layer is the radial basis function radbas,and the transfer function of the output layer is linear function Purelin.The results showed that:RBF neural network prediction model has higher precision.,Through multivariate statistical analysis on measured samples and prediction samples,the average values of As,Ni,Zn in the study area were found to exceed the background values of the soil environment in Shanghai City,and the As elements reached a high degree of variation,and Pb,Zn and Ni reached moderate variation.Through factor analysis,the first 2 factors basically contain the main information of all the elements.Among the first factors,the maximum load is the element Ni (0.946),and the second factor is the element As (0.930).Through the evaluation of potential ecological risk index,the study area is found to in a slight ecological risk level.The RBF neural network model can be used to reduce the cost of sampling and analysis,and to evaluate the ecological risk of heavy metals in the regional soil.