Keywords

macroinvertebrates, habitat preferences, ecological modelling

Start Date

1-7-2002 12:00 AM

Description

To meet the requirements of the EU Water Framework Directive, models are useful to predict communities in watercourses based on the abiotic characteristics of their aquatic environment. For that purpose back-propagation artificial neural network algorithms were used to induce predictive models on a dataset of the Zwalm river basin (Flanders, Belgium). This dataset consisted of 120 samples, collected over a two year period. Fifteen environmental variables including temperature, percentage of dissolved oxygen, water depth, stream velocity, presence/absence of hollow beds, … were measured at each site, as well as the abundance of the aquatic macroinvertebrate taxa. Different neural networks were developed and optimised to obtain the best model configuration for the prediction of the presence/absence of macroinvertebrate taxa. The best performing number of hidden layers and neurons, transfer functions in the hidden and output layer and training algorithms have been searched for. The different options were theoretically and practically validated and assessed. The theoretical validation was based on cross-validation. For the practical validation, potential applications of the neural network models were analysed, and the predictive performance of the models was assessed using ecological expert knowledge. The obtained results indicate that the number of times a taxon was found in the whole river basin influences the architecture of the network. The presence of the very rare taxon Aplexa and the absence of the very common taxon Tubificidae are predicted better by the Levenberg- Marquardt algorithm while Asellidae which are moderately frequent are predicted better by the gradient descent algorithm. One may also conclude that not all network models result in a relevant relation between a variable and a specific taxon. For Gammaridae for example, a rather small ANN structure gave a better idea of the impact of dissolved oxygen on its presence than a larger one. More reliable predictions and ecological interpretations for river ecosystem management would thus be possible provided the best configuration could be found.

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Jul 1st, 12:00 AM

Optimisation of Artificial Neural Network (ANN) model design for prediction of macroinvertebrate communities in the Zwalm river basin (Flanders, Belgium)

To meet the requirements of the EU Water Framework Directive, models are useful to predict communities in watercourses based on the abiotic characteristics of their aquatic environment. For that purpose back-propagation artificial neural network algorithms were used to induce predictive models on a dataset of the Zwalm river basin (Flanders, Belgium). This dataset consisted of 120 samples, collected over a two year period. Fifteen environmental variables including temperature, percentage of dissolved oxygen, water depth, stream velocity, presence/absence of hollow beds, … were measured at each site, as well as the abundance of the aquatic macroinvertebrate taxa. Different neural networks were developed and optimised to obtain the best model configuration for the prediction of the presence/absence of macroinvertebrate taxa. The best performing number of hidden layers and neurons, transfer functions in the hidden and output layer and training algorithms have been searched for. The different options were theoretically and practically validated and assessed. The theoretical validation was based on cross-validation. For the practical validation, potential applications of the neural network models were analysed, and the predictive performance of the models was assessed using ecological expert knowledge. The obtained results indicate that the number of times a taxon was found in the whole river basin influences the architecture of the network. The presence of the very rare taxon Aplexa and the absence of the very common taxon Tubificidae are predicted better by the Levenberg- Marquardt algorithm while Asellidae which are moderately frequent are predicted better by the gradient descent algorithm. One may also conclude that not all network models result in a relevant relation between a variable and a specific taxon. For Gammaridae for example, a rather small ANN structure gave a better idea of the impact of dissolved oxygen on its presence than a larger one. More reliable predictions and ecological interpretations for river ecosystem management would thus be possible provided the best configuration could be found.