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

分布式数据分析用于实时监测和检测智能城市中的山洪Nirmalya Roy，马里兰州大学巴尔的摩县风暴和洪水造成了世界上70%的自然灾害。这些自然灾害平均每年影响多达2亿人，每年造成的经济损失在500亿至1，000亿美元之间。在人口稠密地区主动监测这些山洪暴发易发地区，并及时向城市官员提供通知，可以帮助有效地优先考虑、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，环境危害和积水的形成，并防止任何周围财产，房屋的损坏和生命损失。该项目是建立一个便携式无线传感器网络为基础的硬件系统，以监测水位上升，设计分布式预测数据分析算法与人在回路中，并提供智能手机为基础的真实的时间通知城市官员。该项目建议通过考虑目标事件的实时上下文信息，将来自微博馈送的人类观察整合到洪水事件的表现形式中。该项目将与霍华德县和巴尔的摩县地方政府官员合作，部署在埃利科特市和巴尔的摩市的几个山洪暴发易发地区。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（如推文）的结合。首先，该项目旨在建立预测数据分析技术，以预测山洪暴发的严重程度。其次，该项目调查的适当性，和代表性的微博服务，并从社会网络网站与实时山洪事件相关的饲料。第三，该项目通过人工生成的事实和来自微博服务的观察来增强基于多模态传感器的数据分析模型。我们设计了它的部署，用于检测和评估真实的环境中的紧急洪水情况。PI团队正在与城市合作伙伴合作，衡量和比较现有市政系统或协议在实践中的关键性能改进，以及拟议系统在实际城市使用和演示中的可行性，作为全球城市团队挑战赛计划的一部分。

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.