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

Dimopoulos, Ioannis; Chronopoulos, J.; Chronopoulou-Sereli, A.; Lek, Sovan

doi:10.1016/s0304-3800(99)00099-x

Cited by 141 publications

(68 citation statements)

References 11 publications

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“…The "black box" term is no longer a suitable term, since recent advances in the field of environmental sciences have provided a set of techniques to determine the relative importance of each input variable. These techniques include sensitivity analyses (Scardi, 1996;Lek et al, 1996a;Recknagel et al, 1997), input variable relevancies and neural interpretation diagrams (Ozesmi & Ozesmi, 1999), randomization tests of significance (Olden, 2000;Olden & Jackson, 2002), and partial derivatives (Dimopoulos et al, 1999;Reyjol et al, 2001;Gevrey et al, 2006). All these approaches are based on the fact that the contribution of each independent variable depends on the magnitude and direction of the connection weights between neurons (Olden et al, 2008).…”

Section: Handling Ecological Data With Annsmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANNs) in Aquatic Ecology

Quetglas¹,

Ordines²,

Guijarro³

2011

Artificial Neural Networks - Application

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Section: Handling Ecological Data With Annsmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANNs) in Aquatic Ecology

Quetglas¹,

Ordines²,

Guijarro³

2011

Artificial Neural Networks - Application

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“…This method can thus be seen as a 'local' sensitivity analysis. Gevrey et al (2003), Dedecker et al (2005bDedecker et al ( , 2007 and Beauchard et al (2003) used the 'PaD' method Dimopoulos et al 1999) which consists in a calculation of the partial derivatives of the output according to the input variables. Several authors (Brosse et al 1999(Brosse et al , 2001Dedecker et al 2005bDedecker et al , 2007Gevrey et al 2003;Mastrorillo et al 1997b;Olden and Jackson 2002) applied Garson's algorithm (Garson 1991;Goh 1995).…”

Section: Model Interpretationmentioning

confidence: 99%

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

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To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.

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“…A sensitivity analysis was performed to investigate the relationships between chl a concentration and model input parameters of water quality (Dimopoulos et al 1995 and1999). That is, in order to determine the effect of each parameter on chl a concentration, the input values for the validation site No.…”

Section: Resultsmentioning

confidence: 99%

“…Many algorithms have been proposed to determine the impact of input variables on output (e.g., Garson 1991, Goh 1995, Lek et al 1996aand 1996b, Balls et al 1996, Seginer 1997, Dimopoulos et al 1995and 1999. The profile and percentage contributions of input variables determined by the Lek et al (1996a and1996b) and Dimopoulos (1995 and1999) algorithms allow explanation of the impact of a set of environmental variables in algal bloom modeling. In this work, we used ANN for an ecologically relevant explanatory process (i.e., to determine the contribution of the input variables to explain variations of the output), and we found that COD was the most influential input factor contributing to change in the output -chl a (i. e., Microcystis spp., algal blooms).…”

Section: Resultsmentioning

confidence: 99%