Predicting flood-induced flow and sediment dynamics using data-driven physics-informed models

使用数据驱动的物理模型预测洪水引起的流量和沉积物动力学

• 批准号: 2233986
• 负责人: Ali Khosronejad
• 金额: $ 55万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233986&HistoricalAwards=false
• 关键词: Predicting; flood; induced; flow; sediment

It is vital to understand the physics of flood flow in rivers. Such understanding can help practicing engineers, researchers, and stakeholders (a) appropriately design infrastructures along and across rivers and (b) better protect the river environment. To understand the flood flow in rivers, this project will develop and utilize artificial intelligence to produce mean flow characteristics of the flood flow and riverbed topography. The developed artificial intelligence algorithms will enable reliable flood flow prediction at a small fraction of the computational cost of existing models. Thus, this research will benefit society by providing practicing engineers and stakeholders with modeling tools to (a) predict flood impacts on the stability of infrastructure in natural waterways and (b) evaluate the efficiency of flood mitigation strategies. The project will support two graduate students for four years and engage 12 undergraduates in summer research activities relevant to machine learning and flood prediction. As a part of this project, underserved adolescents in a non-secure detention center in New York will be trained in computer programing and simple machine learning.The research goals of this project include (a) improving the existing capabilities of high-fidelity numerical modeling tools to enable physics-based simulations of floods in natural waterways, (b) applying the high-fidelity model to evaluate flood impacts on the stability of infrastructure installed in large-scale waterways, and (c) employing the high-fidelity flood simulation results of large-scale rivers to inform and develop machine learning algorithms for efficient and affordable prediction of flood impacts on infrastructure and potentially transform the field of flood prediction research. This project will involve a group of practitioners from private, local, and federal agencies to learn how to use the developed tools. The research and educational objectives of this proposal will result in (a) advancement of knowledge regarding the complex interactions among flood flow, waterway, vegetation, and infrastructure; (b) promotion of educational performance by (i) engaging underserved adolescents of a non-secure detention center in STEM, (ii) encouraging the participation of underrepresented groups in research, (iii) involving graduate and undergraduate students in multidisciplinary research, and (iv) developing a new interdisciplinary graduate course; and (c) broad dissemination of high-fidelity models and machine learning through online repositories and journal publications for free access by practitioners and the scientific community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

了解河流中洪水流动的物理规律是至关重要的。这样的理解可以帮助实践工程师，研究人员和利益相关者（a）适当地设计基础设施沿着和跨河流和（B）更好地保护河流环境。为了了解河流中的洪水流量，该项目将开发和利用人工智能来产生洪水流量和河床地形的平均流量特性。开发的人工智能算法将使可靠的洪水流量预测在现有模型的计算成本的一小部分。因此，这项研究将有利于社会提供实践工程师和利益相关者的建模工具，以（a）预测洪水对自然水道基础设施的稳定性的影响和（B）评估洪水缓解策略的效率。该项目将为两名研究生提供四年的支持，并让12名本科生参与与机器学习和洪水预测相关的夏季研究活动。作为该项目的一部分，纽约一个非安全拘留中心的未得到充分服务的青少年将接受计算机编程和简单机器学习的培训。该项目的研究目标包括：（a）提高高保真数值建模工具的现有能力，以实现基于物理的自然水道洪水模拟，（B）应用高保真模型来评估洪水对安装在大规模水道中的基础设施的稳定性的影响，以及（c）采用大规模的高保真洪水模拟结果，扩展河流，为基础设施的洪水影响提供信息和开发机器学习算法，以实现高效和经济的预测，并可能改变洪水预报研究领域。该项目将涉及一组来自私人，地方和联邦机构的从业人员，以学习如何使用开发的工具。本提案的研究和教育目标将导致（a）增进关于洪水、水道、植被和基础设施之间复杂相互作用的知识;（B）通过（i）让未得到充分服务的非安全拘留中心青少年参与STEM，（ii）鼓励代表性不足的群体参与研究，㈢让研究生和本科生参与多学科研究，以及㈣开发新的跨学科研究生课程;（c）广泛传播高-保真度模型和机器学习通过在线存储库和期刊出版物供从业者和科学界免费访问。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。