Integrating data-driven ecological models in an expert-based decision support system for water management in the Du river basin (Vietnam).

In this study, classification trees were combined with the Water Framework Directive (WFD)-Explorer, a modular toolbox that supports integrated water management in a river basin to evaluate the impact of different restoration measures on river ecology. First, the WFD-Explorer toolbox analysed the effect of different restoration options on the abiotic river characteristics based on the water and substance balance embedded in the simulation environment. Based on these abiotic characteristics, the biological index Biological Monitoring Working Party for Vietnam was then predicted by classification trees that were trained on biological and abiotic data collected in the Du river basin in northern Vietnam. The ecological status of streams in the basin ranged from nearly pristine headwaters to severely impacted river stretches. Elimination of point sources from ore extraction and decentralised domestic wastewater treatment proved to be the most effective measures to improve the ecological condition of the Du river basin. The combination of the WFD-Explorer results with data-driven models enabled model application in a situation where expert knowledge was lacking. Consequently, this approach appeared promising for decision support in the context of river restoration and conservation management.

Distribution and mobilization of pollutants in the sediment of a constructed floating wetland used for treatment of combined sewer overflow events.

Sediments in combined sewer overflow treatment systems may exhibit elevated pollutant concentrations. Concentrations measured in the sediment of a floating treatment wetland ranged from 0.17 to 1.6 (cadmium), 28 to 142 (copper), 10 to 33 (chromium), 50 to 141 (manganese), 5 to 20 (nickel), 50 to 203 (lead), and 185 to 804 (zinc) mg/kg dry matter and 7.4 to 17 (iron), 2 to 8 (total nitrogen), and 1.3 to 4.4 (total phosphorus) g/kg dry matter. During overflow events, the entering water volumes can disturb the sediments. A greenhouse experiment was set up to evaluate the possible mobilization of pollutants through disturbation. The disturbation did not result in an increased mobilization of cadmium, copper, chromium, nickel, lead, zinc, nitrogen, phosphorus, and organic carbon towards the pore and surface water. Calcium concentrations in the surface water increased for all sediments, as a result of release from the exchangeable sediment pool and dissolution of carbonates. Geochemical speciation modeling indicated that, in the pore water, the free ion form was the most abundant for calcium, iron, manganese, cadmium, and nickel, with its fraction increasing with time.

Model-based design of horizontal subsurface flow constructed treatment wetlands: a review.

The increasing application of constructed wetlands for wastewater treatment coupled with increasingly strict water quality standards is an ever growing incentive for the development of better process design tools. This paper reviews design models for horizontal subsurface flow constructed treatment wetlands, ranging from simple rules of thumb and regression equations, to the well-known first-order k-C* models, Monod-type equations and more complex dynamic, compartmental models. Especially highlighted in this review are the model constraints and parameter uncertainty. A case study has been used to demonstrate the model output variability and to unravel whether or not more complex but also less manageable models offer a significant advantage to the designer.

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.

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.

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.

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.

Overview and quantification of the factors affecting the upstream and downstream movements of Gammarus pulex (Amphipoda).

Human activities have severely deteriorated the Flemish river systems, and many functions such as drinking water supply, fishing, ... are threatened. Because their restoration entails drastic social (e.g. change in habits with regard to water use and discharge, urban planning) and economical (e.g. investment in nature restoration, wastewater treatment system installation) consequences, the decisions should be taken with enough forethought. Ecosystem models can act as interesting tools to support decision-making in river restoration management. In particular models that can predict the habitat requirements of organisms are of considerable importance to ensure that the planned actions have the desired effects on the aquatic ecosystems. In preliminary studies, Artificial Neural Network (ANN) models were tested and optimized to obtain the best model configuration for the prediction of the habitat suitability for Gammarus pulex based on the abiotic characteristics of their aquatic environment in the Zwalm river basin (Flanders, Belgium). Although, these ANN models are in general quite robust with a rather high predictive reliability, the model performance has to be increased with regard to simulations for river restoration management. In particular, spatial-temporal expert-rules have to be included. Migration kinetics (downstream drift and upstream migration) of the organism and migration barriers along the river (weirs, impounded river sections, ...) can deliver important additional information on the effectiveness of the restoration plans, and also on the timing of the expected effects. This paper presents an overview and quantification of the factors affecting the upstream and downstream movements of Gammarus pulex. During further research, ANN models will be used to predict the habitat suitability for Gammarus pulex after several restoration options. The migration models, implemented in a Geographical Information System (GIS), are applied to calculate the migration time to the restored parts of the river. In this way, decision makers have an idea whether and when a selected restoration option has the desired effect.

Model study of short-term dynamics of secondary treatment reed beds at Saxby (Leicestershire, UK).

Relatively simple black-box models, such as the well-known k-C* model, are commonly applied to design horizontal sub-surface flow constructed treatment wetlands. Important shortcomings of this model are the oversimplification of reality on the one hand, and the inability to predict short-term effluent dynamics on the other. A possible solution for these drawbacks could be the application of dynamic compartmental models. This article reports on the calibration requirements and the simulation results of such a dynamic model. A quantitative sensitivity analysis was used to identify the most sensitive parameters after which model predictions were optimized by adjusting those parameter values. Model fits were acceptable but missed some of the short-term dynamics observed in reality. At this point, it might therefore still be unwise to use the model as a design tool. Further model adjustments and calibration efforts are needed to enhance its reliability.

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.