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

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