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.

了解河流中洪水流的物理学至关重要。这种理解可以帮助实践工程师，研究人员和利益相关者（a）沿河流和跨河流的适当设计基础设施，以及（b）更好地保护河流环境。为了了解河流的洪水流，该项目将发展并利用人工智能来产生洪水流和河床地形的平均流量特征。开发的人工智能算法将以现有模型的计算成本的一小部分来实现可靠的洪水流预测。因此，这项研究将通过为实践工程师和利益相关者提供建模工具来使社会受益（a）预测洪水对天然水道基础设施稳定性的影响，并评估缓解洪水的效率。该项目将支持两名研究生四年，并在与机器学习和洪水预测有关的夏季研究活动中吸引12名本科生。 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大规模水道以及（c）使用大型河流的高保真洪水模拟结果为机器学习算法提供信息，以有效且负担得起的洪水对基础设施的影响，并有可能改变洪水预测研究的领域。该项目将涉及来自私人，地方和联邦机构的一群从业者，以学习如何使用开发工具。该提案的研究和教育目标将导致（a）有关洪水，水道，植被和基础设施之间复杂相互作用的知识的发展； （b）（i）（i）与STEM中非安全拘留中心的服务不足的青少年促进教育绩效，（ii）鼓励代表性不足的群体参与研究，（iii）涉及研究生和本科生在多学科研究中，以及（iv）（iv）（iv）开发新的跨学科研究生课程； （c）通过在线存储库和从业人员和科学界免费访问的高保真模型和机器学习的广泛传播。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。