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在智能城市实时监测和检测山洪暴发Nirmalya Roy，马里兰大学巴尔的摩县的风暴和洪水导致了世界上70%的自然灾害。这些自然灾害平均每年影响多达2亿人，每年的经济损失在500亿至1000亿美元之间。在人口稠密地区主动监测这些易发山洪的地区，并向城市官员提供及时通知，有助于有效地优先处理、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，防止对周围财产、房屋的破坏和生命损失。该项目正在建设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，设计环路中有人参与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订阅的人类观察，通过考虑目标事件的实时背景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，在埃利科特市和巴尔的摩市的几个山洪易发地区部署。这项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(如推文)的交汇点。首先，该项目旨在建立预测性数据分析技术，以预测山洪暴发的严重程度。其次，该项目调查微博服务的适当性和代表性，以及与实时山洪事件相关的社交网站订阅。第三，该项目使用人为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。我们设计了它的部署，用于在真实环境中检测和评估突发洪水情况。PI团队正在与城市合作伙伴合作，测量和比较在实践中相对于现有市政系统或协议的关键性能改进，以及作为全球城市团队挑战计划的一部分，拟议系统在城市实际使用和示范方面的可行性。

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

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

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