Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium
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, e...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2002-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/tsw.2002.79 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849397032210399232 |
|---|---|
| author | Andy P. Dedecker Peter L.M. Goethals Niels De Pauw |
| author_facet | Andy P. Dedecker Peter L.M. Goethals Niels De Pauw |
| author_sort | Andy P. Dedecker |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a9b5d4d43e9c4f279aa063e86dd2e6d6 |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2002-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-a9b5d4d43e9c4f279aa063e86dd2e6d62025-08-20T03:39:10ZengWileyThe Scientific World Journal1537-744X2002-01-0129610410.1100/tsw.2002.79Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, BelgiumAndy P. Dedecker0Peter L.M. Goethals1Niels De Pauw2Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, J. Plateaustraat 22, B-9000 Gent, BelgiumLaboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, J. Plateaustraat 22, B-9000 Gent, BelgiumLaboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, J. Plateaustraat 22, B-9000 Gent, BelgiumModelling 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.http://dx.doi.org/10.1100/tsw.2002.79 |
| spellingShingle | Andy P. Dedecker Peter L.M. Goethals Niels De Pauw Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium The Scientific World Journal |
| title | Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium |
| title_full | Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium |
| title_fullStr | Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium |
| title_full_unstemmed | Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium |
| title_short | Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium |
| title_sort | comparison of artificial neural network ann model development methods for prediction of macroinvertebrate communities in the zwalm river basin in flanders belgium |
| url | http://dx.doi.org/10.1100/tsw.2002.79 |
| work_keys_str_mv | AT andypdedecker comparisonofartificialneuralnetworkannmodeldevelopmentmethodsforpredictionofmacroinvertebratecommunitiesinthezwalmriverbasininflandersbelgium AT peterlmgoethals comparisonofartificialneuralnetworkannmodeldevelopmentmethodsforpredictionofmacroinvertebratecommunitiesinthezwalmriverbasininflandersbelgium AT nielsdepauw comparisonofartificialneuralnetworkannmodeldevelopmentmethodsforpredictionofmacroinvertebratecommunitiesinthezwalmriverbasininflandersbelgium |