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.

用于实时监测和检测智慧城市山洪的分布式数据分析 Nirmalya Roy，马里兰大学巴尔的摩县 风暴和洪水造成了世界 70% 的自然灾害。这些自然灾害平均每年影响多达 2 亿人，每年造成的经济损失在 500 亿美元至 1000 亿美元之间。主动监测人口稠密地区的山洪易发地区并向城市官员提供及时通知有助于有效地确定优先顺序、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、环境危害和积水形成，并防止周围财产、房舍受损和人员伤亡。该项目正在构建一个基于便携式无线传感器网络的硬件系统来监测水位上升，设计人机交互的分布式预测数据分析算法，并向城市官员提供基于智能手机的实时通知。该项目建议整合来自微博的人类观察，通过考虑目标事件的实时上下文信息来预测洪水事件的表现。该项目将与霍华德县和巴尔的摩县当地政府官员合作，部署在埃利科特市和巴尔的摩市的几个山洪易发地区。该研究将寻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）的结合。首先，该项目旨在建立预测数据分析技术来预测山洪的严重程度。其次，该项目调查了微博服务的适当性和代表性，以及来自与实时山洪事件相关的社交网站的信息。第三，该项目利用人类生成的事实和来自微博服务的观察来增强基于多模式传感器的数据分析模型。我们设计其部署来检测和评估真实环境中的紧急洪水情况。作为全球城市团队挑战计划的一部分，PI 团队正在与城市合作伙伴合作，衡量和比较实践中现有市政系统或协议的关键性能改进，以及拟议系统在实际城市使用和示范中的可行性。

项目成果

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: 2019-06
• 期刊: 2019 IEEE International Conference on Smart Computing (SMARTCOMP
• 作者: Anam, Amrita;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: 2020-09
• 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

A Flash Flood Categorization System Using Scene-Text Recognition

使用场景文本识别的山洪分类系统

• DOI: 10.1109/smartcomp.2018.00085
• 发表时间: 2018-06-01
• 期刊: 2018 IEEE International Conference on Smart Computing (SMARTCOMP)
• 作者: Bipendra Basnyat;Nirmalya Roy;A. Gangopadhyay
• 通讯作者: A. Gangopadhyay

Evaluating Disaster Time-Line from Social Media with Wavelet Analysis

使用小波分析从社交媒体评估灾难时间线

• DOI: 10.1109/smartcomp.2018.00072
• 发表时间: 2018-06
• 期刊: 2018 IEEE International Conference on Smart Computing (SMARTCOMP
• 作者: Anam, Amrita;Gangopadhyay, Aryya;Roy, Nirmalya
• 通讯作者: Roy, Nirmalya

Analyzing the Emotions of Crowd for Improving the Emergency Response Services

分析人群情绪以改善应急响应服务

• DOI: 10.1016/j.pmcj.2019.04.009
• 发表时间: 2019-08
• 期刊: Pervasive and mobile computing
• 作者: Neha Singh; Nirmalya Roy
• 通讯作者: Nirmalya Roy