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.

随着气候变化加剧了与城市增长相关的环境挑战，绿色雨水基础设施（GSI）是一种普遍的雨水缓解策略，可提供弹性并减轻发展对洪水的影响。同时，完全可持续的GSI系统必须面对历史上不平等基础设施分布的挑战。当前的数据革命已到达市政雨水计划；但是，这些程序受到GSI生命周期动态，高性能和新兴计算工具的知识的限制，以及如何将新科学整合到设计和计划决策中。 GSI设计，绩效功能和规划决策之间形成的空间存在科学差距，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了一个新的GSI动态模型，该模型考虑了社会和环境公平问题。该模型将翻转基础设施计划的范式，并将对社会和环境的影响与工程解决方案相同。该模型将被公共和私人从业人员使用，以计划，开发和管理更可持续和公平的GSI，并通过研究人员加深对与城市洪水相关的复杂社会问题的收敛知识。 GSI研究的当前状态已经成熟，可以应用AI技术来推进GSI知识，以辨别关键参数，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。对于这个项目，土木工程师，计算机科学家和地理学家正在加入建立一个新平台，该平台在具有多种数据模式的动态环境中使用AI，从其空间和时间特征到数据类型。该研究框架承认了GSI及其与周围社区的高相互依存关系和联系的更广泛含义，并旨在通过股票感知的AI模型来改善GSI设计的社会正义。该项目将通过将GSI绩效数据和全市范围的开放数据组合并应用机器学习方法来开发适用于美国的预测模型，使用大型GSI监视关系数据库（位于Villanova University）。这项工作是通过预测GSI在城市环境中的一系列条件和限制方面的性能来提高对GSI动态的理解，以公平地最大程度地提高GSI社区的利益。 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法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。