I-Corps: Cloud-based Water Resources Simulations

该项目小组计划进一步开发一种新的地下水建模技术，这种技术有可能大大降低地下水可持续性调查的成本，并可能改变地下水的管理方式、数据的收集方式，甚至人们的合作方式。在这一倡议下，项目团队开发了一个基于云的地下水模拟系统，该系统作为一种服务而不是产品提供，并与一个大规模的预处理空间数据库实时链接，允许人们放大数据库覆盖的任何地方，对地下水流量和污染物运输进行初步预测。此外，该软件允许用户将自己的数据添加到服务提供的数据中，改进模型预测并允许聚合用户数据，从本质上讲，将用户转变为共同开发人员/合作者，并作为他们使用该应用程序的副作用创造价值。由于可持续性是21世纪最大的挑战之一，最近的IT和数据革命为理解和管理复杂系统、相互作用和可持续性的能力提供了前所未有的潜在突破机会。然而，这些可能性在很大程度上没有实现，主要是因为产生大数据的技术与分析大数据的技术不匹配。项目团队最近与密歇根环境质量部合作，开发了地下水科学与工程的综合计算基础设施。该合作利用了现有项目的优势——全州范围的数据库开发和数据集成，来自州、联邦和其他机构仓库的网络，以及计算技术的进步，特别是基于分层gis的建模、随机建模、实时可视化仿真和数据分析。在项目团队方面，利用整合这些努力的协同作用，显著改善我们的知识创造基础设施，以理解、表征、建模和可视化复杂的地下水系统。

The project team plans to further develop a new enabling technology for groundwater modeling - one that has the potential to substantially reduce the costs of groundwater sustainability investigations and could change how groundwater is managed, how data is collected, and even how people collaborate. Under this initiative, the project team has developed a cloud-based groundwater simulation system, that is delivered as a service, not as a product, and is live-linked to a massive pre-processed spatial database, allowing one to zoom in anywhere covered by the database to make a preliminary prediction of groundwater flow and contaminant transport. Additionally, the software allows users to add their own data to that provided by the service, refining model predictions and allows aggregating user data, essentially turning users into co-developers/collaborators and building value as a side-effect of their use of the application.As sustainability is among the greatest challenges in the 21st century and recent IT and data revolution offers unprecedented opportunities for potential breakthroughs in the ability to understand and manage complex systems, interactions, and sustainability. These possibilities, however, are largely unrealized, primarily because the technologies to produce big data are not matched by technologies to analyze them. The project team recently partnered with the Michigan Department of Environmental Quality to develop this integrated computational infrastructure in groundwater science and engineering. The collaboration takes advantage of existing programs - statewide database development and data integration from a network of state, federal, and other institutional storehouses on the part of the State and computational advancements, specifically in hierarchical GIS-based modeling, stochastic modeling, and real-time visual simulation and data analytics, on the part of the project team - and exploits the synergies of integrating these efforts to significantly improve our knowledge-creating infrastructure to understand, characterize, model, and visualize complex groundwater systems.