Collaborative Research: A Physics-Informed Flood Early Warning System for Agricultural Watersheds with Explainable Deep Learning and Process-Based Modeling

合作研究：基于物理的农业流域洪水预警系统，具有可解释的深度学习和基于过程的建模

• 批准号: 2243776
• 负责人: Ibrahim Demir
• 金额: $ 20万
• 依托单位: University of Iowa
• 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 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243776&HistoricalAwards=false
• 关键词: Collaborative; Research; Physics; Informed; Flood

Global floods and extreme rainfall events have surged by more than 50% this decade and are now occurring at a rate four times higher than in 1980. However, the capability of physical models in predicting flood events remains limited across spatial scales, especially in intensively managed agricultural systems like the Midwestern U.S. The apparent disparity between observed seasonal patterns of extreme precipitation and high streamflow events presents a challenge when using precipitation alone to predict flood occurrence and severity. This project addresses a fundamental question in hydrologic science: how do watershed characteristics and in-land management practices regulate the precipitation-runoff relationship across agriculture-dominated watersheds? The modeling framework in this project will integrate the complex impacts of watershed characteristics, human land use, and management practices into hydrological prediction. An early warning system will be developed for projecting flood occurrence at a granular level in a managed system and will be shared for further evaluation of the flood forecasting performance and uncertainty assessment.The overarching goal of the research is to develop a data-driven, physics-informed early warning system to predict flood occurrence and support communities in agriculture-dominated watersheds across the Midwestern United States. This project will develop a graph-based transformer deep learning approach integrated with process-based hydro-ecological modeling to improve flood prediction accuracy and keep the interpretable structure. The results of the project will be tested, shared, and deployed as a real-time prediction tool on a web-based platform that integrates mapping capabilities, advanced visualizations, and mobile access. The early warning system will be accessible to multiple users, especially underrepresented communities, concerning the direct impacts of flooding on life and property and the indirect effects on the food security, economy, and livelihood of the communities.This project is jointly funded by Hydrologic Sciences, the Established Program to Stimulate Competitive Research (EPSCoR), and the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.

这个十年来，全球洪水和极端降雨事件飙升了50％以上，现在以比1980年的速度高四倍。但是，在空间尺度上，物理模型预测洪水事件的能力在整个空间尺度上仍然有限，尤其是在诸如美国中西部之类的强度管理的农业体系中，在美国中西部遇到的季节性陷入困境时，在陷入困境的季节性陷入困境中，遇到了极端的陷入困境，而遇到了越来越高的陷入困境。该项目解决了水文科学中的一个基本问题：流域特征和国内管理实践如何规范以农业为主的流域之间的降水跑关系？该项目中的建模框架将将流域特征，人类土地使用和管理实践的复杂影响整合到水文预测中。将开发一个预警系统，用于在托管系统中将洪水预测在颗粒状水平上，并将共享以进一步评估洪水预测的绩效和不确定性评估。该研究的总体目标是开发数据驱动的物理，物理性的，具有的早期预测系统，以预测农业跨国公司的洪水范围的洪水和支持社区，跨国公司跨越了跨米德尔氏派对跨米德尔氏派。该项目将开发一种基于图的变压器深度学习方法，该方法与基于过程的水力生态模型集成在一起，以提高洪水预测的准确性并保持可解释的结构。该项目的结果将在基于Web的平台上作为实时预测工具进行测试，共享和部署，该平台集成了映射功能，高级可视化和移动访问。 The early warning system will be accessible to multiple users, especially underrepresented communities, concerning the direct impacts of flooding on life and property and the indirect effects on the food security, economy, and livelihood of the communities.This project is jointly funded by Hydrologic Sc​​iences, the Established Program to Stimulate Competitive Research (EPSCoR), and the Directorate for Geosciences to support AI/ML advancement in the地球科学。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影响的评论标准的评估来支持的。