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.

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.