CAREER: Harnessing Heterogeneous Sources of Data and Artificial Intelligence for Informed Flood Management

职业：利用异构数据源和人工智能进行明智的洪水管理

• 批准号: 2238639
• 负责人: Erfan Goharian
• 金额: $ 52.08万
• 依托单位: University of South Carolina at Columbia
• 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: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238639&HistoricalAwards=false
• 关键词: CAREER; Harnessing; Heterogeneous; Sources; Data; CAREER; Harnessing; Heterogeneous; Sources; Data

This Faculty Early Career Development (CAREER) project supports the nation's research priority in climate adaptation through advancing scientific understanding of urban and coastal floodings and establishing a new generation of intelligent flood early warning systems and smart flood control infrastructure. The project will address gaps in flood data availability and in present-day flood modeling by harnessing heterogeneous data sources, such as ground-based images and videos taken by traffic cameras, smartphones and drones, to provide faster and more distributed flood timeseries data and visual information. The use of multi-source, heterogeneous data, along with accelerated flood modeling and data analytics, will support the transformation of existing flood infrastructure into Active Flood Control Infrastructure through real-time model updating, active measurements, and active flood management. The project integrates research activities with educational and outreach plans to (i) train the next generation of AI-enabled engineers and scientists, (ii) foster flood-aware communities, and (iii) inform decision-makers by developing an integrated looped learning framework consisting of four phases of flood modeling, planning and response, behavioral analysis, and virtual reality gameplay and outreach. The project will study two coastal watersheds in South Carolina, one dominantly urbanized and the other natural, with potential transferability to other urban and coastal systems.Novel Artificial Intelligence and image processing tools will be deployed to process different types of inputs at different stages of flood management: data acquisition, flood detection, monitoring, simulation, and forecasting. The research core of this project revolves around the detailed design and development of an integrated modeling platform consisting of Multi-Deep Learning Models (MDLM) for flood data analysis, modeling, and management. In the data analysis phase, deep learning models, alone or in combination with the reconstruction of a 3-Dimensional of the study area, will be used to provide numerical data, such as water levels, and inundation area, from ground-based images, and videos. Moreover, a multi-source data fusion module will be developed to feed data from different satellites and bands to a multi-branch deep learning network for flood detection and feature extraction. In the modeling phase, this CAREER project will enhance the understanding of compound flooding by developing a fully coupled model for simulating coastal compound floods through the integration of distributed hydrologic and surface hydraulics mathematical models into a single modeling framework. Then, a set of machine learning-based surrogate models will be developed to mimic the knowledge of fine-scale physics-based flood models and provide timely flood predictions. Finally, this project will provide adaptive design guidelines to turn existing infrastructure into active flood control infrastructure through real-time model updating, active measurements, and active flood management. New technologies and tools created in this project will allow stakeholders, decision-makers, and the public to make choices regarding their direct and indirect involvement pre-, peri-, and post-flooding and evaluate their impacts using integrated numerical simulations and virtual reality gameplay.This CAREER project is jointly funded by the Civil Infrastructure Systems (CIS) and the Established Program to Stimulate Competitive Research (EPSCoR) programs.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.

这项教师早期职业发展（职业）项目通过推进对城市和沿海洪水的科学理解并建立新一代智能洪水预警系统和智能洪水控制基础设施，从而支持全国气候适应的研究重点。该项目将通过利用异质数据源（例如，通过交通摄像机，智能手机和无人机拍摄的视频）来解决洪水数据可用性的差距以及当今的洪水建模，以提供更快，更分布的洪水泛滥的时间工程数据和视觉信息。通过实时模型更新，主动测量和主动洪水管理，使用多源，异质数据以及加速的洪水建模和数据分析，将支持现有的洪水基础设施转换为主动洪水控制基础架构。该项目将研究活动与教育和外展计划相结合，以（i）培训下一代AI支持的工程师和科学家，（ii）培养洪水感知的社区，以及（iii）通过开发一个集成的循环学习框架来为决策者提供信息，该框架由洪水建模，计划和响应，行为分析以及虚拟现实的四个阶段组成，这些阶段的四个阶段。 该项目将研究南卡罗来纳州的两个沿海流域，一个主要城市化，另一种是自然的，可能会转移到其他城市和沿海系统。设备的人工智能和图像处理工具将被部署，以处理不同类型的洪水管理阶段的不同类型的输入：数据采集，洪水检测，监测，监测，模拟，模拟，模拟和预测。该项目的研究核心围绕着由多深度学习模型（MDLM）组成的集成建模平台的详细设计和开发，用于洪水数据分析，建模和管理。在数据分析阶段，深度学习模型单独或与研究区域的3维的重建结合起来，将用于提供数值数据，例如水位和淹没区，来自地面图像和视频。此外，将开发多源数据融合模块，以将数据从不同的卫星和频段馈送到一个多支支深度学习网络，以进行洪水检测和特征提取。在建模阶段，该职业项目将通过开发一个完全耦合的模型来增强对复合洪水的理解，以通过将分布式水文和表面液压学数学模型整合到单个建模框架中，从而模拟沿海化合物洪水。然后，将开发一组基于机器的替代模型，以模仿基于物理学的洪水模型的知识，并提供及时的洪水预测。最后，该项目将提供自适应设计指南，以通过实时模型更新，主动测量和主动洪水管理将现有基础架构转化为主动洪水控制基础架构。该项目中创建的新技术和工具将允许利益相关者，决策者和公众就其直接和间接参与的选择做出选择，并在盆栽前和盆栽后，并使用成熟的数值模拟和虚拟现实游戏玩法来评估其影响，并由本职业生涯进行了奖励。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。