Veronique Adriaenssens

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.

Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study

Fuzzy ordered classifiers were used to assign fuzzy labels to river sites expressing their suitability as a habitat for a certain macroinvertebrate taxon, given up to three abiotic properties of the considered river site. The models were built using expert knowledge and evaluated on data collected in the Province of Overijssel in the Netherlands. Apart from a performance measure for crisp classifiers common in the aquatic ecology domain, the percentage of correctly classified instances (% CCI), two performance measures for fuzzy (ordered) classifiers are introduced in this paper: the percentage of correctly fuzzy classified instances (% CFCI) and the average deviation (AD). Furthermore, results of an interpretability-preserving genetic optimization of the linguistic terms, applying once binary encoding and once real encoding, are presented.

Uncertainty in ecological status assessments of lakes and rivers using diatoms

The EU’s Water Framework Directive requires all surface water bodies to be classified according to their ecological status. As biological communities show both spatial and temporal heterogeneity, expressions of ecological status will, inevitably, have an element of uncertainty associated with them. A consequence of this environmental heterogeneity is that there is a risk that status inferred from one or more samples is different to the true status of that water body. In order to quantify the scale of temporal uncertainty associated with benthic diatoms, replicate samples were collected from sites across the ecological status gradient in lakes and rivers in the UK. Variability (expressed as standard deviation of temporal replicate samples from a single site) could be described using a polynomial function and this was then used to calculate the risk of placing a water body in the wrong ecological status class. This risk varied depending on the distance from the class boundaries and the number of replicates. Based on these data, we recommend that ecological status is determined from a number of samples collected from a site over a period of time.

Application of clustering techniques for the characterization of macroinvertebrate communities to support river restoration management

The European Water Framework Directive prescribes that the development of a river assessment system should be based on an ecological typology taking the biological reference conditions of each river type as a starting point. Aside from this assessment, water managers responsible for river restoration actions also need to know the steering environmental factors to meet these reference conditions for biological communities in each ecological river type. As such, an ecological typology based on biological communities is a necessity for efficient river management. In this study, different clustering techniques including the Sørensen similarity ratio, ordination analysis and self-organizing maps were applied to come to an ecological classification of a river. For this purpose, a series of sites within the Zwalm river basin (Flanders, Belgium) were monitored. These river sites were then characterized in terms of biotic (macroinvertebrates), physical–chemical and habitat variables. The cluster analysis resulted in a series of characteristic biotic communities that are found under certain environmental conditions, natural as well as human-influenced. The use of multiple clustering techniques can be of advantage to draw more straightforward and robust conclusions with regard to the ecological classification of river sites. The application of the clustering techniques on the Zwalm river basin, allowed for distinguishing five mutually isolated clusters, characterized by their natural typology and their pollution status. On the basis of this study, one may conclude that river management could benefit from the use of clustering methods for the interpretation of large quantities of data. Furthermore, the clustering results might enable the development of a cenotypology useful for efficiently steering river restoration and enabling river managers to meet a good ecological status in most of the rivers as set by the European Water Framework Directive.

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.