Distributed Data Analytics for Real-Time Monitoring and Detection of Flash Floods in Smart City

用于实时监测和检测智慧城市山洪的分布式数据分析

• 批准号: 1640625
• 负责人: Nirmalya Roy
• 金额: $ 22.5万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640625&HistoricalAwards=false
• 关键词: Distributed; Data; Analytics; Real; Time

Distributed Data Analytics for Real-Time Monitoring and Detection of Flash Floods in Smart CityNirmalya Roy, University of Maryland Baltimore County Storms and floods cause 70% of the world's natural disasters. These natural disasters affect on average up to 200 million people in a year, with economic losses between US$50 billion to US$100 billion annually. Monitoring these flash flood prone areas proactively in populated areas and providing just-in-time notification to the city officials can help in effectively prioritizing, controlling, and mitigating such disastrous events. Solving flash floods problems helps improve the flow of traffic, reduce traffic congestion, environmental hazards, and formation of stagnant water, and prevent damages of any surrounding properties, premises, and loss of lives. This project is building a portable wireless sensor network-based hardware system to monitor the rising level of water, designing distributed predictive data analytics algorithms with human in the loop, and providing smartphone based real time notifications to city officials. The project proposes to integrate human observations from micro-blogging feeds to forecast the manifestation of the flood events by considering real-time contextual information of the target event. The project will be deployed in several flash flood prone areas in Ellicott City and Baltimore City in partnership with the Howard County and Baltimore County local government officials. The research will pursue the junction of wireless sensor networks based data analytics and real-time social network data such as tweets. First, the project aims to build predictive data analytics techniques to forecast the severity of flash floods. Second, the project investigates the appropriateness, and representation of micro-blogging services, and feeds from social network sites associated with real-time flash flood events. Third, the project augments the multi-modal sensor-based data analytics model with human generated facts and observations from micro-blogging services. We engineer its deployment for detecting and assessing the emergent flood situation in real environments. The PI team is collaborating with city partners to measure and compare the key performance improvements over existing municipal systems or protocols in practice and the viability of the proposed system for practical city usage and demonstration as part of the Global City Team Challenge program.

分布式数据分析，用于实时监测和检测Smart Citynirmalya Roy，马里兰州巴尔的摩大学的暴风雨和洪水造成世界自然灾害的70％。这些自然灾害一年平均影响多达2亿人，经济损失在500亿至1000亿美元之间。在人口稠密的地区积极监视这些易于洪水泛滥的地区，并向市政府官员提供即时通知，可以有效地优先考虑，控制和减轻此类灾难性事件。解决山洪问题有助于改善交通流量，减少交通拥堵，环境危害以及停滞的水的形成，并防止任何周围财产，场所和生命损失的损害。该项目正在构建一种基于便携式的无线网络硬件系统，以监视水平上升水平，并在循环中与人类设计分布式的预测数据分析算法，并向城市官员提供基于智能手机的实时通知。该项目建议通过考虑目标事件的实时上下文信息来整合人类的观察结果，以预测洪水事件的体现。该项目将与霍华德县和巴尔的摩县地方政府官员合作，将部署在埃利科特市和巴尔的摩市的几个山洪繁荣地区。该研究将追求基于无线传感器网络的数据分析和实时社交网络数据（例如推文）的交界。首先，该项目旨在建立预测数据分析技术，以预测山洪的严重性。其次，该项目调查了微博客服务的适当性和表示，以及与实时山洪事件相关的社交网站的供稿。第三，该项目通过人类生成的事实和微博客服务的观察增强了基于多模式传感器的数据分析模型。我们正在设计其部署，以检测和评估实际环境中的新兴洪水情况。 PI团队正在与城市合作伙伴合作，以衡量和比较实践中现有的市政系统或协议的关键绩效改进，以及拟议的实用城市使用系统的可行性，作为全球城市团队挑战计划的一部分。

项目成果

A Flash Flood Categorization System Using Scene-Text Recognition

• DOI: 10.1109/smartcomp.2018.00085
• 发表时间: 2018-06
• 期刊: 2018 IEEE International Conference on Smart Computing (SMARTCOMP)
• 作者: Bipendra Basnyat;Nirmalya Roy;A. Gangopadhyay

Analyzing Social Media Texts and Images to Assess the Impact of Flash Floods in Cities

分析社交媒体文本和图像以评估山洪对城市的影响

• DOI: 10.1109/smartcomp.2017.7946987
• 发表时间: 2017
• 期刊: 2017 IEEE International Conference on Smart Computing (SMARTCOMP
• 作者: Basnyat, Bipendra;Anam, Amrita;Singh, Neha;Gangopadhyay, Aryya;Roy, Nirmalya
• 通讯作者: Roy, Nirmalya

Flood Detection Framework Fusing The Physical Sensing & Social Sensing

融合物理传感的洪水检测框架

• DOI: 10.1109/smartcomp50058.2020.00080
• 发表时间: 2020
• 期刊: 2020 IEEE International Conference on Smart Computing (SMARTCOMP
• 作者: Singh, Neha;Basnyat, Bipendra;Roy, Nirmalya;Gangopadhyay, Aryya
• 通讯作者: Gangopadhyay, Aryya

Analyzing The Emotions of Crowd For Improving The Emergency Response Services

• DOI: 10.1016/j.pmcj.2019.04.009
• 发表时间: 2019-08
• 期刊: Pervasive Mob. Comput.
• 作者: Neha Singh;Nirmalya Roy;A. Gangopadhyay

Identifying the Context of Hurricane Posts on Twitter using Wavelet Features

使用小波特征识别 Twitter 上飓风帖子的上下文

• DOI: 10.1109/smartcomp.2019.00072
• 发表时间: 2019
• 期刊: 2019 IEEE International Conference on Smart Computing (SMARTCOMP
• 作者: Anam, Amrita;Gangopadhyay, Aryya;Roy, Nirmalya
• 通讯作者: Roy, Nirmalya