Planning: SCC-PG: Smart, Sustainable, and Equitable Green Stormwater Systems in Urban Communities
规划:SCC-PG:城市社区智能、可持续和公平的绿色雨水系统
基本信息
- 批准号:2228035
- 负责人:
- 金额:$ 14.93万
- 依托单位:
- 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:2022-10-01 至 2024-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Urban communities are increasingly including Green Stormwater Infrastructure (GSI) in their watershed management plans to manage stormwater in cities. Stormwater programs are scientifically limited by a lack of knowledge of the longevity of GSI, how real-time adaptive control can improve performance, and lack of process for using collected data in new GSI designs and policy. Further, there is no scientifically robust method to consider social equity in GSI design and planning. To overcome these challenges, and achieve sustainable stormwater management, solutions must use other available technologies in new ways that are co-created with community members woven into the planning-design-implementation process. The project hypothesizes that this challenge can be met using smart systems that can: 1) create and expand opportunities for GSI, 2) improve the sustainable function of these systems, and 3) address community infrastructure needs and preferences to overcome issues of inequity. The main objective of this planning proposal is to develop a roadmap to combat the community-stormwater challenge. This project will accomplish this by forming a task force, Community Science Work Group, of a cross-disciplinary team of researchers, government agencies, community and industry partners to create a roadmap to develop a set of computer technologies and tools to design smart, sustainable, community driven, equitable GSI systems for urban communities. This will be accomplished through continuously engaging with community stakeholders to incorporate preferences and technical and societal interactions (e.g., GSI co-benefits) at all levels. This project will establish channels to build and engage a project team for a future proposal to effectively use technology in urban environments to respond to the stated community needs. This current project will also explore if emerging computing technologies can help meet community-stormwater challenges through an iterative stakeholder engagement process, which would lead to new science in urban stormwater systems and a new avenue for application of computer technology. This project will broaden community understanding and engagement in GSI infrastructure, increase GSI ecosystem services and community resilience, and ultimately improve the urban environment and contribute to social equity. Through tightly intertwined cross-disciplinary research and outreach goals, this project provides a transformative benefit to society by providing a fair and open community driven platform to improve cities’ efforts to effectively address federal water quality and safety needs and establish new frontiers for urban sustainability. This planning project will serve as a vital start to build a platform to alleviate the community-stormwater challenge.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.
城市社区越来越多地包括绿色雨水基础设施(GSI)在城市中的流域管理雨水中,由于缺乏对GSI缺乏新的GSI设计和政策中使用收藏家数据的过程的知识,因此有限制了RMWATER计划和计划。要克服挑战,并实现可持续的雨水管理,必须以新的方式使用可用的技术,这些技术与CONUNITY成员共同创建,可以使用可以使用智能系统来满足规划 - 启用。 )为GSI创造和扩大机会,2)改善这些系统的可持续功能,3)SAL的主要目标是开发与社区 - 史蒂姆沃特挑战的路线图。一个工作队,社区。科学工作组,由研究人员,政府机构,社区和行业合作伙伴组成对于城市的社区,将与利益相关者共同完成,以融合偏好,技术和社交互动g。社区的需求。社会公平和宣传目标,通过提供一个公平的开放式平台来改善城市的水质和安全的新领域,以减轻社区挑战。反映了NSF'Stututory的使命,并在使用基金会的知识分子和更广泛的影响评论标准方面值得一提。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Virginia Smith其他文献
Guardrail Baselines for Unlearning in LLMs
法学硕士遗忘的护栏基线
- DOI:
10.48550/arxiv.2403.03329 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Pratiksha Thaker;Yash Maurya;Virginia Smith - 通讯作者:
Virginia Smith
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold
针对不正确合成数据的强化学习将 LLM 数学推理的效率提高了八倍
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Amrith Rajagopal Setlur;Saurabh Garg;Xinyang Geng;Naman Garg;Virginia Smith;Aviral Kumar - 通讯作者:
Aviral Kumar
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
Grass:使用结构化稀疏梯度计算高效的低内存 LLM 训练
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Aashiq Muhamed;Oscar Li;David Woodruff;Mona Diab;Virginia Smith - 通讯作者:
Virginia Smith
Is Support Set Diversity Necessary for Meta-Learning?
支持集多样性对于元学习是必要的吗?
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Amrith Rajagopal Setlur;Oscar Li;Virginia Smith - 通讯作者:
Virginia Smith
Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning
通过联合多任务学习从多个智能家居学习情境感知策略
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Tian;Tian Li;Yuqiong Sun;Susanta Nanda;Virginia Smith;Vyas Sekar;S. Seshan - 通讯作者:
S. Seshan
Virginia Smith的其他文献
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{{ truncateString('Virginia Smith', 18)}}的其他基金
Equipment: MRI: Track 2 Acquisition of a Hydraulic and Sediment Recirculation Flume to Advance Fundamental Research in Urban Stormwater and Fluvial Processes
设备: MRI:轨道 2 获取水力和沉积物再循环水槽,以推进城市雨水和河流过程的基础研究
- 批准号:
2320356 - 财政年份:2023
- 资助金额:
$ 14.93万 - 项目类别:
Standard Grant
CAREER: Foundations of Federated Multi-Task Learning
职业:联合多任务学习的基础
- 批准号:
2145670 - 财政年份:2022
- 资助金额:
$ 14.93万 - 项目类别:
Continuing Grant
CAS- Climate: CDS&E: Facilitating Sustainable and Fair Transformation of GSI through AI
CAS-气候:CDS
- 批准号:
2152834 - 财政年份:2022
- 资助金额:
$ 14.93万 - 项目类别:
Standard Grant
Collaborative Research: An Inter-disciplinary Approach to Constraining Paleo-geomorphic Responses to the Eocene-Oligocene Hothouse to Icehouse Transition
合作研究:限制始新世-渐新世温室向冰室转变的古地貌响应的跨学科方法
- 批准号:
1844180 - 财政年份:2019
- 资助金额:
$ 14.93万 - 项目类别:
Standard Grant
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