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Computational Intelligence and Neuroscience
Volume 2016, Article ID 3264587, 9 pages
http://dx.doi.org/10.1155/2016/3264587
Research Article

Social Media Meets Big Urban Data: A Case Study of Urban Waterlogging Analysis

Computer Science and Technology Institute, Zhejiang University, Hangzhou 310058, China

Received 10 May 2016; Revised 3 July 2016; Accepted 31 August 2016

Academic Editor: Trong H. Duong

Copyright © 2016 Ningyu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the design and development of smart cities, opportunities as well as challenges arise at the moment. For this purpose, lots of data need to be obtained. Nevertheless, circumstances vary in different cities due to the variant infrastructures and populations, which leads to the data sparsity. In this paper, we propose a transfer learning method for urban waterlogging disaster analysis, which provides the basis for traffic management agencies to generate proactive traffic operation strategies in order to alleviate congestion. Existing work on urban waterlogging mostly relies on past and current conditions, as well as sensors and cameras, while there may not be a sufficient number of sensors to cover the relevant areas of a city. To this end, it would be helpful if we could transfer waterlogging. We examine whether it is possible to use the copious amounts of information from social media and satellite data to improve urban waterlogging analysis. Moreover, we analyze the correlation between severity, road networks, terrain, and precipitation. Moreover, we use a multiview discriminant transfer learning method to transfer knowledge to small cities. Experimental results involving cities in China and India show that our proposed framework is effective.