Visual and Machine Analytics for Environmental Monitoring

用于环境监测的视觉和机器分析

• 批准号: 566261-2021
• 负责人: Gutwin, Carl
• 金额: $ 10.36万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721615
• 关键词: Visual; Machine; Analytics; Environmental; Monitoring

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.

废弃油田或化学品泄漏等受污染的陆地场地是一个主要的环境问题。加拿大有20多万个受污染的场地，通过土壤和水污染造成健康问题，并造成数十亿美元的环境和经济损失。受污染的场地必须进行修复以去除污染物，合作伙伴环境材料科学（EMS）开发了一种新的生物修复方法，比现有方法更有效，成本更低。他们的方法的一个关键要素是比其他方法更频繁地监测当前的污染水平，以便让利益相关者确定补救措施是否正常工作，并预测场地何时符合要求。从污染场地的传感器收集的信息用于污染场地的模型和可视化，以帮助利益相关者做出决策。然而，目前的建模和可视化技术在很大程度上没有在环境背景下进行测试-大量的变量，生成准确模型的困难，每个状态存在的多维不确定性以及许多不同的利益相关者为分析带来了新的和困难的挑战。 该项目将开发分析技术，可以在污染现场监测和修复的背景下取得成功。我们的主要成果将是新的软件工具，实现新的建模，分析和可视化技术，这将大大提高EMS的修复解决方案在现实世界中的有效性，从而更快地修复加拿大的污染场地。我们有三个研究目标：传感器分析，以改善现场传感器数据的解释，汇总和表征;在高度不确定的领域更准确的预测模型;以及表示数据，模型和不确定性的可视化技术，使多个利益相关者能够对污染场地做出更好的决策。