Industrial Transformation Training Centres - Grant ID: IC190100031
Funder
Australian Research Council
Funding Amount
$3,973,202.00
Summary
ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understand ....ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understanding and quantifying uncertainty plays in the process. The aim of Data Analytics in Resources and Environment (DARE) is to develop and deliver the data science skills and tools for Australia’s resource industries to make the best possible evidence-based decisions in exploiting and stewarding the nation’s natural resources.Read moreRead less
Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection ....Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection in water-quality data and generating predictions of sediment and nutrient concentrations throughout river networks in near-real time. This will represent a fundamental increase in scientific knowledge, which will be immediately useful in the domains of aquatic science, environmental monitoring, and statistics.Read moreRead less