Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece)

The aim of the present work is to propose a model for the estimation of lead concentration in grasses using urban descriptors easily accessible and to study the specific effect of each descriptor on lead concentration. Six descriptors were considered: the density of vegetation, the vegetation height, wind velocity, height of building, distance of adjacent street, traffic volume. Lead concentrations were determined in one grass species, Cynodon dactylon (L.) Pers, (Bermuda grass), collected from 30 different locations in Athens city. The proposed model is a multilayer perceptron (MLP) trained by backpropagation. The predictive quality of the model was judged by two cross-validation methods. The generalization ability of the model is confirmed by a determination coefficient higher than 0.91. The study of the first partial derivatives of the output of the MLP with respect to each input is used to identify of the factors influencing the lead concentration and the mode of action of each factor. Results allow to classify the environmental descriptors by their decreasing influence on lead concentration: distance of adjacent street, traffic volume, density of vegetation, wind velocity, height of building and vegetation height. © 1999 Elsevier Science B.V. All rights reserved.