Environmental impact prediction using neural network modelling. An example in wildlife damage

Authors


Dr François Spitz, Institut de Recherche sur les Grands Mammifères, INRA, B.P. 27, 31326 Castanet-Tolosan cedex, France. E-mail: spitz@teleirgm.toulouse.inra.fr

Abstract

1. Decision making in management of environmental impact confronts the problem of analysing relationships in highly complex ecological systems. These relationships are generally non-linear, and conventional techniques do not apply satisfactorily. The problem is equivalent to that of predicting the output of a black box. Artificial neural networks (ANN) have shown tremendous promise in modelling such situations.

2. The present work describes the development and validation of an ANN in modelling wildlife damage to farmland, a particular instance of ecological impact. An ANN approach was developed and tested using data from 200 damaged plots, and a control sample of 20 undamaged plots, described by 17 environmental characters. The dependent variable was the financial cost of impact per plot.

3. The predictive quality of the ANN models was evaluated through the ‘leave-one-out’ procedure. For 82% of predicted values, deviation from observed values is lower than 1780, that is 10% of the range of the observed values. The frequency of bad predictions depends on the minimum level of impact (critical effect size) considered. In France, compensation is given starting from a minimum level of 200 FF. At this level, the frequency of occurrence of Type I errors (predicted impact does not occur) is 7·11%. Different strategies of prevention of impact, using different threshold values for prevention, are analysed. Frequency of Type I errors increases, and that of Type II errors (occurrence of unpredicted impact) decreases as the threshold for prevention increases.

4. Sensitivity analysis allows the determination of the effect of seven quantitative variables on the cost of damage compensation. Proximity of a paved road, proximity and number of houses, and number of other buildings contribute negatively, and three other variables (proportion of the perimeter of the plot occupied by woody vegetation, density of the vegetation, and density of wild boar in the surrounding area) contribute positively to the predicted value.

5. Results show that the utility of impact prediction for prevention depends on the cost of errors and the absolute cost of prevention.

6. Finally, ANNs proved able to learn complex relationships between environmental variables and impact assessment, and to produce operationally relevant predictions. Good predictions can help managers to distribute efficiently their actions between prevention, protection and compensation.

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