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.
Artificial Neural Network models (ANNs) were used to predict habitat suitability for 12 macroinvertebrate taxa, using environmental input variables. This modelling technique was applied to a dataset ...of 102 measurement series collected in 31 sampling sites in the Greek river Axios. The database consisted of seven physical-chemical and seven structural variables, as well as abundances of 90 macroinvertebrate taxa. A seasonal variable was included to allow the description of potential temporal changes in the macroinvertebrate communities. The induced models performed well for predicting habitat suitability of the macroinvertebrate taxa. Senso-nets and sensitivity analyses revealed that dissolved oxygen concentration and the substrate composition always played a crucial role in predicting habitat suitability of the macroinvertebrates. Although ANNs are often referred to as black box prediction techniques, it was demonstrated that ANNs combined with sensitivity analyses can provide insight in the relationship between river conditions and the occurrence of macroinvertebrates, and thus deliver new ecological knowledge. Consequently, these models can be useful in decision-making for river restoration and conservation management.
Relationships between land-use and river water quality assessed by means of biological and physical-chemical variables and habitat characteristics were analysed for the Zwalm River basin in Flanders ...(Belgium). The research focussed on three zones within this river basin, each characterized by different land uses, and consequently, different types of pollution, mainly of diffuse origin. Environmental data have been integrated within a Geographic Information System. Possible relationships between aquatic ecosystem and land-use variables were searched for by means of multivariate analysis.
The Dender basin in Flanders (Belgium) was used as a case study to implement the European Union (EU) Water Framework Directive. During the last 5 years, ample research on pollution loads and ...ecological water quality has been done on the Dender River. In addition to biological sampling of macroinvertebrates and fish, automated measurement stations were also used to investigate the spatial-temporal variability of the physical-chemical water quality. This research revealed that the pollution of the Dender River is highly variable. The high nutrient loads result in severe algae blooms during summer, leading to very complex diurnal processes. In this paper, the monitoring strategy for the assessment of the biological water quality in the Dender basin has been reviewed in relation to the EU Water Framework Directive. For this, seasonal macroinvertebrate data were collected and assessed. General trends and hidden structures in these data were analysed by means of classification trees, using different inputs (seasons, river types, and subbasins). Validation of the results was obtained by applying statistical methods. Analysis about the presence and abundance of the macroinvertebrates revealed that there is a distinct difference between the biological water quality in the Dender stem river and its tributaries. There are also seasonal differences between the macroinvertebrate communities when the Dender and its tributaries are examined separately. An optimised monitoring strategy is proposed based on these results and the EU Water Framework Directive. This includes two monitoring campaigns in summer and winter every 3 years. Furthermore, a cyclic monitoring scheme was developed to minimise sampling efforts.
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