MUSIC Webinar Series: MUSIC Optimisation
Friday 31 March 2017
A series of free webinars on water sensitive urban design using MUSIC, presented by industry experts.
Analysing stormwater system trade-offs with MUSIC optimisation
Presenter: Michael Di Matteo, Research Associate (Civil Engineering), University of Adelaide
Stormwater harvesting (SWH) is an important, water-sensitive urban design (WSUD) approach, and SWH system designs must account for trade-offs between several objectives. Important trade-offs may include those between lifecycle cost, harvest volume, and water quality improvement performance. Performance is dependent on design decisions for the type, size, and spatial distribution of stormwater best management practices (BMPs). This webinar discusses a conceptual design modelling framework for MUSIC by eWater that handles the optimal placement of stormwater harvesting infrastructure within an urban development. The framework produces preliminary SWH system designs and multiobjective trade-off curves that can show which catchment locations, in combination, provide the best performance. Highlights include how MUSIC can be linked with an optimisation algorithm to show trade-offs between different systems layouts, simple hacks to save over 90% of computational run time, links to open-source algorithms, and other applications of optimisation for WSUD planning.
Michael is a postgraduate Research Associate specialising in urban water resources optimisation. He recently submitted a PhD thesis titled Multiobjective Planning and Design of Distributed Stormwater Harvesting and Treatment Systems through Optimization and Visual Analytics. He has conceptualised and developed decision support software for WSUD, focusing on showing system trade-offs between multiple WSUD objectives. The research includes an optimisation framework featuring a multiobjective genetic algorithm linked with MUSIC, which was recently published in an American Society of Civil Engineers journal. Michael is interested in making optimisation and machine learning methods more accessible for WSUD planning and design practice. Some of his other interests include urban water for climate adaptation, and opportunities to include WSUD in Smart Cities initiatives.