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Peter L M Goethals

Publications and source records attributed to Peter L M Goethals.

9 recordsLinked to original sources
PubMed2005

Application of artificial neural network models to analyse the relationships between Gammarus pulex L. (Crustacea, Amphipoda) and river characteristics.

This study aimed at analysing the relationship between river characteristics and abundance of Gammarus pulex. To this end, four methods which can identify the relative contribution and/or the contribution profile of the input variables in neural networks describing the habitat preferences of this species were compared: (i) the "PaD" ("Partial Derivatives") method consists of a calculation of the partial derivatives of the output in relation to the input variables; (ii) the "Weights" method is a computation using the connection weights of the backpropagation Artificial Neural Networks; (iii) the "Perturb" method analyses the effect of a perturbation of the input variables on the output variable; (iv) the "Profile" method is a successive variation of one input variable while the others are kept constant at a fixed set of values. The dataset consisted of 179 samples, collected over a three-year period in the Zwalm river basin in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Gammarus pulex were used in this study. The different contribution methods gave similar results concerning the order of importance of the input variables. Moreover, the stability of the methods was confirmed by gradually removing variables. Only in a limited number of cases a shift in the relative importance of the remaining input variables could be observed. Nevertheless, differences in sensitivity and stability of the methods were detected, probably as a result of the different calculation procedures. In this respect, the "PaD" method made a more severe discrimination between minor and major contributing environmental variables in comparison to the "Weights", "Profile" and "Perturb" methods. From an ecological point of view, the input variables "Ammonium" and to a smaller extent "COD", were selected by these methods as dominant river characteristics for the prediction of the abundance of Gammarus pulex in this study area.

Amphipoda↗
PubMed2004

Fuzzy rule-based models for decision support in ecosystem management.

To facilitate decision support in the ecosystem management, ecological expertise and site-specific data need to be integrated. Fuzzy logic can deal with highly variable, linguistic, vague and uncertain data or knowledge and, therefore, has the ability to allow for a logical, reliable and transparent information stream from data collection down to data usage in decision-making. Several environmental applications already implicate the use of fuzzy logic. Most of these applications have been set up by trial and error and are mainly limited to the domain of environmental assessment. In this article, applications of fuzzy logic for decision support in ecosystem management are reviewed and assessed, with an emphasis on rule-based models. In particular, the identification, optimisation, validation, the interpretability and uncertainty aspects of fuzzy rule-based models for decision support in ecosystem management are discussed.

Conservation of Natural Resources↗
PubMed2002

Optimisation of the monitoring strategy of macroinvertebrate communities in the river Dender, in relation to the EU Water Framework Directive.

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.

Animals↗
PubMed2002

Assessment of land-use impact on macroinvertebrate communities in the Zwalm River basin (Flanders, Belgium) using multivariate analysis and geographic information systems.

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.

Agriculture↗
PubMed2002

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, 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.

Amphipoda↗