CAS- Climate: CDS&E: Facilitating Sustainable and Fair Transformation of GSI through AI

CAS-气候：CDS

• 批准号: 2152834
• 负责人: Virginia Smith
• 金额: $ 49.95万
• 依托单位: Villanova University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152834&HistoricalAwards=false
• 关键词: CAS; Climate; CDS& Facilitating; Sustainable

As climate change exacerbates environmental challenges associated with urban growth, green stormwater infrastructure (GSI) is a prevalent stormwater mitigation strategy to provide resilience and mitigate the impacts of development on flooding. In parallel, fully sustainable GSI systems must confront the challenges of historically unequitable distribution of infrastructure. The current data revolution has reached municipal stormwater programs; however, these programs are limited by a lack of knowledge of GSI life-cycle dynamics, high performance and emerging computational tools, and how to integrate new science into design and planning decisions. There is a scientific gap in the space formed among GSI design, performance function, and planning decisions that requires bridging hydrologic science, urban planning, and data analytics. This project leverages innovations in artificial intelligence (AI), advancements in the empirical and theoretical understanding of urban hydrologic science, and social data to produce a new model of GSI dynamics that considers social and environmental equity issues. This model will flip the paradigm of infrastructure planning and put the impact on society and the environment on par with engineering solutions to flooding. The model will be made available for use by public and private practitioners to plan, develop, and manage more sustainable and equitable GSI, and by researchers to deepen convergent knowledge of the complex social issues associated with urban flooding. The current state of GSI research is ripe for the application of AI techniques to advance GSI knowledge to discern key parameters, optimize GSI design and development, and enable future performance forecasts in a changing environment. For this project, civil engineers, computer scientists, and geographers are joining together to produce a new platform that uses AI in a dynamic environment with multiple data modalities, ranging from their spatial and temporal characteristics to data types. The research framework acknowledges the wider implications of GSI and its high interdependency and connection to the surrounding community and aims to improve social justice of GSI design through an equity-aware AI model. This project will use a large GSI monitoring relational database (housed at Villanova University) by combining GSI performance data and city-wide open data and applying machine learning methods to develop predictive models applicable across the US. This work targets advancing understanding of GSI dynamics by forecasting the performance of GSIs for a given array of conditions and constraints in urban settings to equitably maximize GSI community benefits. The project will support a diverse faculty team and engage students, urban communities, and industry and academic colleagues by: (1) creating state-of- the-art research and mentoring opportunities for graduate and undergraduate students from underrepresented backgrounds, (2) developing and delivering GSI learning modules for practitioners, and (3) integrating and promoting issues of equity and sustainability within urban stormwater management.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.

随着气候变化加剧与城市发展相关的环境挑战，绿色雨水基础设施（GSI）是一种流行的雨水缓解策略，可提供抵御能力并减轻发展对洪水的影响。与此同时，完全可持续的 GSI 系统必须面对历史上基础设施分配不公平的挑战。当前的数据革命已经影响到市政雨水计划；然而，这些项目由于缺乏对 GSI 生命周期动态、高性能和新兴计算工具以及如何将新科学融入设计和规划决策的了解而受到限制。 GSI 设计、性能功能和规划决策之间存在科学差距，需要将水文科学、城市规划和数据分析联系起来。该项目利用人工智能 (AI) 创新、城市水文科学实证和理论理解的进步以及社会数据，产生考虑社会和环境公平问题的 GSI 动态新模型。该模型将颠覆基础设施规划的范式，将对社会和环境的影响与洪水工程解决方案相媲美。该模型将供公共和私人从业者使用，以规划、开发和管理更可持续和公平的 GSI，并供研究人员使用，以加深对与城市洪水相关的复杂社会问题的聚合知识。 GSI 研究的现状已经成熟，可以应用人工智能技术来推进 GSI 知识，以识别关键参数、优化 GSI 设计和开发，并在不断变化的环境中实现未来的性能预测。在这个项目中，土木工程师、计算机科学家和地理学家共同开发了一个新平台，该平台在动态环境中使用人工智能，具有多种数据模式，从空间和时间特征到数据类型。该研究框架承认 GSI 的更广泛影响及其与周围社区的高度相互依赖性和联系，旨在通过具有公平意识的人工智能模型改善 GSI 设计的社会正义。该项目将使用大型 GSI 监控关系数据库（位于维拉诺瓦大学），结合 GSI 性能数据和全市开放数据，并应用机器学习方法来开发适用于全美国的预测模型。这项工作的目标是通过预测 GSI 在城市环境中给定条件和约束条件下的表现来增进对 GSI 动态的理解，以公平地最大化 GSI 社区利益。该项目将支持多元化的教师团队，并通过以下方式吸引学生、城市社区、行业和学术同事：(1) 为来自弱势背景的研究生和本科生创造最先进的研究和指导机会，(2) 为从业者开发和提供 GSI 学习模块，以及 (3) 在城市雨水管理中整合和促进公平和可持续发展问题。该奖项反映了 NSF 的 法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。