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

Neural network models to study relationships between lead concentration in grasses and permanent... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

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

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Publisher
Elsevier
Copyright
Copyright © 1999 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(99)00099-X
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Ecological ModellingElsevier

Published: Aug 17, 1999

References

  • Variations in plant and soil lead and cadmium content in urban parks in Athens, Greece
    Chronopoulos, J.; Haidouti, C.; Chronopoulou-Sereli, A.; Massas, I.

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