SCC-Planning: Agent-based Scenario Planning for a Smart & Connected Community against Sea Level Rise in Tampa Bay

SCC-Planning：基于智能体的场景规划

• 批准号: 1737598
• 负责人: Yasin Yilmaz
• 金额: $ 9.85万
• 依托单位: University of South Florida
• 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=1737598&HistoricalAwards=false
• 关键词: SCC; Planning; Agent; based; Scenario

Sea-level rise and flooding pose significant risks to communities and infrastructure in Florida and many other coastal states. The work in this proposal seeks to engage a variety of stakeholders in the Tampa Bay region, from citizens to businesses to government agencies, in exercises grounded in Big Data analytics and agent-based modeling that simulates the dynamics and uncertainty of responses to sea level change. The ultimate objective is to create a more connected community by convening these stakeholders and having them work together towards crafting resilient responses to the possible outcomes of sea level rise scenarios. In addition to bringing together scientists, government officials, and business owners, we will also inform the general public, educate K-12 students, outreach to the minority groups about the challenges resulting from sea level rise and coastal flooding due to extreme weather events and natural disasters such as hurricanes and flash floods through a simplified version of the developed software, which is fostered by attractive visual features.The prospective research proposed in this work will provide the much needed foundational science and technology for tackling the potentially disastrous effects of coastal flooding. It will develop novel Big Data analysis techniques and game-theoretic frameworks to deliver an agent based decision-making model which will underlie a scenario planning software for facilitating the community engagement. The proposed decision-making model will integrate fundamental disciplines including oceanic, atmospheric, and marine sciences, civil and environmental engineering, urban planning, and the social sciences into the multi-disciplinary scenario planning framework. The community engagement will be handled from an integrated systems perspective.

海平面上升和洪水对佛罗里达州和许多其他沿海州的社区和基础设施构成了重大风险。 该提案中的工作旨在与坦帕湾地区的各种利益相关者参与，从公民到企业再到政府机构，以大数据分析和基于代理的建模为基础的练习，这些练习模拟了对海平面变化的反应的动态和不确定性。最终目标是通过召集这些利益相关者并让他们共同努力来制定对海平面上升崛起情景可能结果的弹性反应来创建一个更加联系的社区。除了将科学家，政府官员和企业主汇聚在一起之外，我们还将告知公众，对K-12学生进行教育，向少数群体宣讲有关海平面上升和沿海洪水造成的挑战，这是由于飓风和洪水（例如飓风）（如洪水）的范围和洪水泛滥等自然灾害，并通过启动的技术来培养了这项工作。沿海洪水的潜在灾难性影响。它将开发新颖的大数据分析技术和游戏理论框架，以提供基于代理的决策模型，该模型将是一个方案计划软件，以促进社区参与。拟议的决策模型将将包括海洋，大气和海洋科学，民用和环境工程，城市规划以及社会科学在内的基本学科整合到多学科的场景规划框架中。社区参与将从集成系统的角度来处理。

项目成果

Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements

• DOI: 10.1109/access.2019.2939735
• 发表时间: 2019-09
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: A. Nassar;Yasin Yılmaz

Anomaly Detection in Partially Observed Traffic Networks

• DOI: 10.1109/tsp.2019.2892026
• 发表时间: 2019-03-15
• 期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
• 影响因子: 5.4
• 作者: Hou, Elizabeth;Yilmaz, Yasin;Hero, Alfred O., III
• 通讯作者: Hero, Alfred O., III

Distributed Dynamic State Estimation and LQG Control in Resource-Constrained Networks

• DOI: 10.1109/tsipn.2018.2801460
• 发表时间: 2018-02
• 期刊: IEEE Transactions on Signal and Information Processing over Networks
• 影响因子: 3.2
• 作者: Yasin Yılmaz;M. N. Kurt;Xiaodong Wang

Resource Allocation in Fog RAN for Heterogeneous IoT Environments Based on Reinforcement Learning

• DOI: 10.1109/icc.2019.8761626
• 发表时间: 2019-05
• 期刊: ICC 2019 - 2019 IEEE International Conference on Communications (ICC)
• 作者: A. Nassar;Y. Yilmaz

Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid

• DOI: 10.1109/tifs.2018.2854745
• 发表时间: 2019-02-01
• 期刊: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
• 影响因子: 6.8
• 作者: Kurt, Mehmet Necip;Yilmaz, Yasin;Wang, Xiaodong
• 通讯作者: Wang, Xiaodong