Visual and Machine Analytics for Environmental Monitoring

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Contaminated terrestrial sites such as abandoned oilfields or chemical spills are a major environmental problem. Canada's 200,000+ contaminated sites cause health problems through soil and water pollution, and represent a multi-billion dollar environmental and economic liability. Contaminated sites must be remediated to remove pollutants, and partner Environmental Material Science (EMS) has developed a novel bio-remediation approach that is far more effective and far less expensive than current methods. A key element to their approach is monitoring current levels of contamination much more frequently than other methods, in order to allow stakeholders to determine whether remediation is working correctly, and to predict when a site will come into compliance. The information gathered from sensors at the contaminated site is used in models of the contaminated site, and in visualizations that assist stakeholders in making decisions. However, current modeling and visualization techniques are largely untested in the environmental context - and the large number of variables, the difficulties in generating accurate models, the multi-dimensional uncertainty present at each state, and the many different stakeholders present new and difficult challenges for analytics. This project will develop analytics techniques that can succeed in the context of contaminated-site monitoring and remediation. Our main outcome will be novel software tools that implement new modeling, analytics, and visualization techniques, that will substantially improve the effectiveness of EMS's remediation solutions in real-world operation, leading to much faster remediation of Canada's contaminated sites. We have three research objectives: sensor analytics to improve interpretation, aggregation, and characterization of site sensor data; predictive models that are more accurate in highly-uncertain domains; and visualization techniques that represent data, models, and uncertainty to enable multiple stakeholders to make better decisions about contaminated sites.