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Title: |
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Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium |
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Authors: |
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Dedecker, Andy P.; Goethals, Peter L.M.; De Pauw, Niels |
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Journal: |
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TheScientificWorldJOURNAL |
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Year: |
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2002 |
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Volume: |
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2 |
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Page Range: |
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96-104 |
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Article Type: |
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Research Article |
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Domains: |
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Environmental Management & Policy
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Ecosystems and Communities
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Freshwater Systems
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DOI: |
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10.1100/tsw.2002.79 |
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 ?
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article research -
freshwater systems -
proceedings -
research article -
symposium
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Synopsis: |
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Artificial neural networks were applied to predict macroinvertebrate communities in the Zwalm River basin (Flanders, Belgium). Different training and validation methods were applied for model development and assessment. Sensitivity analyses were carried out to study the impact of the input variables on the prediction of presence or absence of macroinvertebrate taxa. |
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Keywords: |
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neural networks, model validation, ecological modeling, sensitivity analyses |
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Abstract |
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Modelling has become an interesting tool to support decision making in water management. River ecosystem modelling methods have improved substantially during recent years. New concepts, such as artificial neural networks, fuzzy logic, evolutionary algorithms, chaos and fractals, cellular automata, etc., are being more commonly used to analyse ecosystem databases and to make predictions for river management purposes. In this context, artificial neural networks were applied to predict macroinvertebrate communities in the Zwalm River basin (Flanders, Belgium). Structural characteristics (meandering, substrate type, flow velocity) and physical and chemical variables (dissolved oxygen, pH) were used as predictive variables to predict the presence or absence of macroinvertebrate taxa in the headwaters and brooks of the Zwalm River basin. Special interest was paid to the frequency of occurrence of the taxa as well as the selection of the predictors and variables to be predicted on the prediction reliability of the developed models. Sensitivity analyses allowed us to study the impact of the predictive variables on the prediction of presence or absence of macroinvertebrate taxa and to define which variables are the most influential in determining the neural network outputs. |
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