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.

由于气候变化加剧了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战略，以提供恢复力并减轻开发对洪水的影响。与此同时，完全可持续的GSI系统必须应对历史上基础设施分布不公平的挑战。当前的数据革命已经影响到市政雨水计划;然而，这些计划受到缺乏GSI生命周期动力学知识、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限制。在GSI设计、性能功能和规划决策之间形成的空间中存在科学差距，需要弥合水文科学、城市规划和数据分析。该项目利用人工智能（AI）的创新，对城市水文科学的经验和理论理解的进步，以及社会数据，产生一个考虑社会和环境公平问题的GSI动态新模型。这种模式将颠覆基础设施规划的范式，并将对社会和环境的影响与洪水的工程解决方案相提并论。该模型将提供给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，并由研究人员加深与城市洪水相关的复杂社会问题的趋同知识。GSI研究的当前状态已经成熟，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正在共同开发一个新的平台，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，并旨在通过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性能数据和全市范围的开放数据，并应用机器学习方法开发适用于美国各地的预测模型。这项工作的目标是通过预测城市环境中特定条件和限制条件下全球太阳能机构的业绩，促进对全球太阳能机构动态的理解，以公平地最大限度地扩大全球太阳能机构社区的利益。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，城市社区以及行业和学术界的同事：（1）为来自代表性不足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性问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。