The application of methods of uncertain reasoning to the biological classification of river water quality

This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.

Assessment of the impact of discharges from surface water sewers on receiving water quality

A broad based approach has been used to assess the impact of discharges to rivers from surface water sewers, with the primary objective of determining whether such discharges have a measurable impact on water quality. Three parameters, each reflecting the effects of intermittent pollution, were included in a field work programme of biological and chemical sampling and analysis which covered 47 sewer outfall sites. These parameters were the numbers and types of benthic macroinvertebrates upstream and downstream of the outfalls, the concentrations of metals in sediments, and the concentrations of metals in algae upstream and downstream of the outfalls. Information on the sewered catchments was collected from Local Authorities and by observation of the time of sampling, and includes catchment areas, land uses, evidence of connection to the foul system, and receiving water quality classification. The methods used for site selection, sampling, laboratory analysis and data analysis are fully described, and the survey results presented. Statistical and graphical analysis of the biological data, with the aid of BMWP scores, showed that there was a small but persistent fall in water quality downstream of the studied outfalls. Further analysis including the catchment information indicated that initial water quality, sewered catchment size, receiving stream size, and catchment land use were important factors in determining the impact. Finally, the survey results were used to produce guidelines for the estimation of surface water sewer discharge impacts from knowledge of the catchment characteristics, so that planning authorities can consider water quality when new drainage systems are designed.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.