Water utilities need a method and framework which enables the identification and prioritisation of research into emerging water quality contaminants and pathogens. This research built a software tool that uses a Multi-Criteria Decision Analysis Bayesian Network model to guide a stakeholder through determining the relative importance of a series of criteria that were selected in consultation with the water industry. The model is flexible and allows the decision-maker to categorise priorities according to utility-relevant factors including time and location.

Although water utilities recognise the value of online instruments that provide real-time monitoring capability, there are problems with visualising and interpreting datasets, and with distinguishing between data resulting from real-world changes in treatment plant operating conditions, for example changed turbidity or flow, and instrument failure. There are also challenges around instrument installation and operation. This project developed tools to support data visualisation and interpretation by building a prototype visualisation platform for analysing complex online UV spectral data in conjunction with weather and lab data (see Factsheet 2 ‘Development of an online platform’). To improve differentiation between instrument failure and real-world data a Bayesian Belief Network model was developed to analyse patterns and variations within datasets. Real operational, high turbidity data was used to demonstrate that this model could accurately identify different causes for the readings which included filter ripening, backwash and other causes (see Factsheet 3 ‘Improving decision making in water plant operability through Bayesian Belief networks’). Strategies for instrument installation and operation were illustrated through case studies.