Water resource management requires access to meteorological data, spatial data such as digital elevation, land use and remote sensing data. Current approaches to water resource management, for the most part, are operating independently whilst generally working towards the same objective (for example, improved water quality in waterways in a region).

While each of these activities has been effective in their own right, there are major benefits and synergies to be gained through a coordinated integration of these tasks. Therefore, it is crucial and promising to deliver research on water resource management and Information and Communication Technologies related issues, including:

  • the framework for the integration of all data and modelling services related to water resource management for a region
  • field monitoring with its associated spatial temporal trend analysis
  • remote sensing (including satellite and autonomous stations)
  • predictive modelling which facilitates and supports management decisions.

Below are some of our current e-Water projects.

Semantics Water Web based on spatial temporal data mining

Adaptive water management requires the assimilation of many different, varied data sets in order to obtain a holistic ("whole-of-water-cycle") view of the environment. The development of Semantics Water Web is used for rapid, seamless integration of the many independently developed water data sources and models based on:

  • spatial temporal data mining
  • wireless sensor networks
  • web services with novel algorithms and computational techniques for the successful analysis of large spatial-temporal water quality databases and the disclosure of interesting knowledge on remote sensing
  • geographical information systems
  • computer cartography
  • environmental assessment 
  • water planning issues underpinning decision makings.

Research team

Spatial-temporal data mining for water resource decision support

The aim of this project is to construct spatial date mining system for water resource decision support based on geospatial and temporal database by improving the quality, completeness, relevance and interpretability of the water data, and building the appropriate spatial data mining model underpinning these decisions.

Spatial data carries topological and/or distance information and it is often organised by spatial indexing structures and accessed by spatial access methods. These distinct features of a spatial database pose challenges and bring opportunities for mining information from spatial data. In this study the spatial and temporal database has been mined using a series of innovative optimisation-based programming algorithm on remote sensing, geographical information system, computer cartography, environmental assessment and planning issues. A further national benefit will be the development of frontier spatial data mining techniques to ensure Australia a leading role in data engineering and knowledge discovery services.

Research team

Data Enhancement, integration and access services for smarter, collaborative and adaptive whole-of-water cycle management

The aim of this project is to improve the speed, rigour and adaptability of the decisions made within South East Queensland by the partners in the Healthy Waterways Partnership by focussing on services that will improve the quality, completeness, relevance and interpretability of the data used in the models underpinning these decisions.

The project is expected to contribute to improved water quality and healthier ecosystems. It is supported by ARC Linkage Project with South East Queensland Healthy Waterways Partnership as an industry partner.

Research team

  • Professor Yanchun Zhang, Director CAI, VU
  • Professor Xiaofang Zhou, University of Queensland
  • Professor Jane Hunter, University of Queensland
  • Associate Professor Shazia Sadiq, University of Queensland
  • Dr Eva Abal, South East Queensland Healthy Waterways