IWM / WSUD | UPCOMING EVENTS

Stormwater Australia Conference | #Stormwater2018

8th - 12th October 2018, Sydney

IWM / WSUD | PAST EVENTS

Smart Metering Workshop | June 2018

Research Symposium 2017 | Forum

Research Symposium 2015 | Forum 

Research Symposium 2014 | Forum

Notable Documents | WSUD

Sharma et al., (2018). Approaches to Water Sensitive Urban Design. 1st Edition.

Hill & Beecham, (2018). The effect of particle size on sediment accumulation in permeable pavements.

Furlong et al., (2018). Understanding the role of the water sector in urban liveability and greening interventions Case studies on Barcelona, Rotterdam, Amsterdam, Copenhagen and Melbourne. 

CRC Water Sensitive Cities, (2018). New software will allow water utilities to work with customers to save water.

CRC Water Sensitive Cities, (2016). Intelligent urban water systems.

Sharma et al., (2016). Water Sensitive Urban Design: An investigationof current systems, implementation drivers, community perceptions and potential to supplement urban water services.

Kazemi & Hill, (2015). Effect of permeable pavement basecourse aggregates on stormwater quality for irrigation reuse.

CRC Water Sensitive Cities, (2015). Using smart meters and data mining to inform demand manaement.

Nichols & Lucke, (2015). Local level stormwater harvesting and reuse: A practical solution to the water security challenges faced by urban trees.

Journal Papers | Smart Metering

1. Cardell-Oliver, R., 2016. A Habit Detection Algorithm (HDA) for Discovering Recurrent Patterns in Smart Meter Time Series. In Big Data Analytics in the Social and Ubiquitous Context: 5th International Workshop on Modeling Social Media, MSM 2014, 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014, and First International Workshop on Machine Learning for Urban Sensor Data, SenseML 2014, Revised Selected Papers (pp. 109-127). Springer International Publishing.
2. Cardell-Oliver, R., Wang, J. and Gigney, H., 2016. Smart Meter Analytics to Pinpoint Opportunities for Reducing Household Water Use. Journal of Water Resources Planning and Management, p.04016007.
3. Wang, J., Cardell-Oliver, R., Liu, W. 2015, 'Discovering routine behaviours in smart water meter data', 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015
4. Cardell-Oliver, R., 2014, September. A habit discovery algorithm for mining temporal recurrence patterns in metered consumption data. In 1st International Workshop on Machine Learning for Urban Sensor Data (SenseML) (Vol. 15).
5. Cardell-Oliver, R.M., Peach, G. 2013, 'Making Sense of Smart Metering Data: A Data Mining Approach for Discovering Water Use Patterns', WATER, 40, 2, pp. 124-128.
6. Cardell-Oliver, R.M. 2013, 'Water use signature patterns for analyzing household consumption using medium resolution meter data', Water Resources Research, 49, 12, pp. 8589-8599.