1. Using Bayesian Networks for Water Quality Prediction in Sydney Harbour Interim Presentation Name: Shannon Watson Supervisors: Ann Nicholson & Charles Twardy

2. Introduction The application domain Bayesian Networks Knowledge Engineering Conclusion

3. The domain Water quality issue within the Sydney community for recreation use Beach watch program to monitor water quality using visual indicators - is pollution showing etc But bacteria sampling shows that visually appealing water can contain harmful pathogens Test beaches for bacteria

4. The domain Deep ocean outfalls were commissioned Reduction in levels of bacteria -improved water quality

5. The domain Beaches in the harbour still have high levels of bacteria though, No bacteria entering through sewage Only surface runoff/storm water overflow Have tried to understand the relationship through modeling

6. Past studies Hose et al. used multi dimension scaling model of Sydney harbour unable to handle the noisy bacteria samples Other models Wasps / CE- QAUL-W2 - used in the US to model estuaries EPA in Sydney want a model that can use knowledge of causal structure

7. Sydney Harbour Modeling the pollution(bacteria) in the harbour - Using Bayesian networks - Can handle noisy data sets - Take advantage of causal structure

8. Bayesian Networks Graphical structure allows non-technical people to understand a technical model. Have been used by R.J. Kennett, a past honours student at Monash, to predict the sea breeze

9. Bayesian Networks – An example

10. Example cont.

11. Knowledge Engineering Used to keep structure of large projects in software engineering discipline Two major implementations -Spiral model/Waterfall Model Demonstrate how Knowledge Engineering can be used in the application of Bayesian Networks, as there is currently no literature on how to create large Bayesian networks

12. Spiral Model

13. Conclusion Use Bayesian networks Create a model of Sydney harbour Aim to predict the water quality for EPA Use Spiral Model Questions