Big Data and Model Supported Environmental Decision Making under Uncertainty

大数据和模型支持不确定性下的环境决策

• 批准号: RGPIN-2017-06960
• 负责人: Chen, Zhi
• 金额: $ 1.53万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636575
• 关键词: Big; Data; Model; Supported; Environmental

Today’s environmental problems including climate changes are getting exponentially more complex. Accordingly, huge volumes of information and data are involved in both daily and strategic environmental management. Based on data, environmental modeling and assessment tools have been playing an essential role in understanding those complex environmental problems and in helping vital decision-making processes. However, the critical gap between the data and the state of the art models has significantly limited the applicability of modeling tools due to data availability and data management issues. For example, the applicant has put forward the following environmental models with reported field applications in the past decade: a multiphase multicomponent chemical fate and transport model for the petroleum-contaminated soil and groundwater, a coupled near- and far- field numerical model for modeling contaminant's dispersion in Canadian offshore water, and a GIS-supported spatial multi-source air quality model with application to California. Data processing costs half or more of the time and efforts for the above-mentioned model developments and applications.

今天的环境问题，包括气候变化，正变得越来越复杂。因此，日常和战略环境管理都涉及大量信息和数据。以数据为基础的环境建模和评估工具在理解这些复杂的环境问题和帮助重要的决策过程中发挥着至关重要的作用。然而，由于数据可用性和数据管理问题，数据和最先进的模型之间的关键差距大大限制了建模工具的适用性。例如，申请人在过去十年中提出了以下环境模型，并报告了现场应用：石油污染土壤和地下水的多相多组分化学归宿和迁移模型，模拟污染物在加拿大近海水域扩散的耦合近场和远场数值模型，以及应用于加州的GIS支持的空间多源空气质量模型。在上述模型开发和应用中，数据处理要花费一半或一半以上的时间和精力。