SCC-Planning: Enhancing Water Resource Management and Infrastructure Improvement through Sensing, Computation, and Community Engagement

SCC-规划：通过传感、计算和社区参与加强水资源管理和基础设施改善

• 批准号: 1737634
• 负责人: Fang Jin
• 金额: $ 9.99万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737634&HistoricalAwards=false
• 关键词: SCC; Planning; Enhancing; Water; Resource

Constructing and maintaining an effective water supply and management system is paramount to enhancing the resiliency of our communities, promoting economic growth in agriculture-dominant regions, and ensuring food security locally, nationally, and worldwide. There are four major socio-technical challenges facing our water systems: i) water shortage, ii) lack of technical and infrastructural integration, iii) disconnects in supply and conservation needs, and iv) inefficient resource management strategies. Stakeholder-driven fundamental research is necessary to overcome these challenges. This interdisciplinary planning project aims to establish a community of stakeholders with the objectives of (1) identifying the grand challenging problems in water supply chain, (2) sharing experiences of modernizing water infrastructures across counties, and (3) addressing the hurdles preventing effective water resource management, especially in the Southwest region of the United States. This capacity building project brings stakeholders including researchers, consumers, water supply companies, and authorities together to share their experience, strengths, and weakness, and to develop a short- and long-term strategic plan for addressing the challenges pertinent to water resource management. The proposed plan will offer a model for states and counties with similar circumstances and thus build a connected community, where stakeholders share their concerns and experiences. Specifically, this planning project will hold (1) a focus group-based study to discuss the persisting, urgent problems among water stakeholders, and (2) a workshop where stakeholders will have the opportunity to learn about challenges from a range of perspective, share advances in water technology and infrastructure, and discover potential research opportunities to address the problems exposed. Thus, this planning project will engage a wide range of water-related stakeholders and promote effective water conservation strategies to protect the region's water supply. By improving water use efficiency, the project will support efforts to meet growing water needs and minimize competition between food, energy, and municipal sectors. The resulting water usage model will help communities develop better programs for water conservation and improve water use efficiency. The project will develop a central area of focus in the water community for future research efforts, and lay the groundwork for much more fruitful collaboration between stakeholders.

建设和维护一个有效的供水和管理系统对于增强我们社区的复原力，促进农业主导地区的经济增长，确保地方，国家和全球的粮食安全至关重要。我们的水系统面临着四个主要的社会技术挑战：i）水资源短缺，ii）缺乏技术和基础设施整合，iii）供应和保护需求脱节，iv）资源管理策略效率低下。 利益相关者驱动的基础研究是克服这些挑战的必要条件。这个跨学科规划项目旨在建立一个利益相关者社区，其目标是（1）确定供水链中具有挑战性的重大问题，（2）分享跨县水基础设施现代化的经验，（3）解决阻碍有效水资源管理的障碍，特别是在美国西南地区。该能力建设项目将包括研究人员、消费者、供水公司和当局在内的利益攸关方聚集在一起，分享他们的经验、优势和弱点，并制定短期和长期战略计划，以应对与水资源管理有关的挑战。拟议的计划将为情况类似的州和县提供一个模式，从而建立一个相互联系的社区，利益攸关方在其中分享他们的关切和经验。具体而言，该规划项目将举行（1）一个基于焦点小组的研究，以讨论水利益相关者之间持续存在的紧迫问题，以及（2）一个研讨会，利益相关者将有机会从一系列角度了解挑战，分享水技术和基础设施的进步，并发现潜在的研究机会，以解决暴露的问题。因此，这一规划项目将使与水有关的广泛利益攸关方参与，并促进有效的节水战略，以保护该区域的供水。通过提高用水效率，该项目将支持满足日益增长的水需求，并尽量减少粮食、能源和市政部门之间的竞争。由此产生的用水模型将帮助社区制定更好的节水计划，提高用水效率。该项目将为水资源界今后的研究工作确定一个中心重点领域，并为利益攸关方之间更富有成效的合作奠定基础。

项目成果

Smart and Connected Water Resource Management via Social Media and Community Engagement

通过社交媒体和社区参与实现智能互联水资源管理

• 发表时间: 2018
• 期刊: The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• 作者: Nguyen, L. H.

Modeling adaptive access control policies using answer set programming

使用答案集编程对自适应访问控制策略进行建模

• DOI: 10.1016/j.jisa.2018.10.007
• 发表时间: 2019
• 期刊: Journal of Information Security and Applications
• 影响因子: 5.6
• 作者: Sartoli, Sara;Namin, Akbar Siami
• 通讯作者: Namin, Akbar Siami

Predict Saturated Thickness using TensorBoard Visualization

• DOI: 10.2312/envirvis.20181135
• 发表时间: 2018
• 作者: V. Nguyen;Tommy Dang;Fang Jin

Spatial-temporal Multi-Task Learning for Within-field Cotton Yield Prediction

• DOI: 10.1007/978-3-030-16148-4_27
• 发表时间: 2018-11
• 作者: L. Nguyen;Jiajie Zhen;Zhe Lin;Hanxiang Du;Zhou Yang;Wenxuan Guo;Fang Jin