Data-Driven Techniques in Disaster Information Management

ACM Computing Surveys - Tập 50 Số 1 - Trang 1-45 - 2018
Tao Li1, Ning Xie2, Chunqiu Zeng2, Wubai Zhou2, Zheng Li2, Yexi Jiang2, Yimin Yang2, Hsin-Yu Ha2, Wei Xue2, Yue Huang3, Shu‐Ching Chen2, Jainendra K. Navlakha2, S. S. Iyengar2
1School of Computing and Information Sciences, Florida International University and School of Computer Science, Nanjing University of Posts and Telecommunications, Jiangsu, P.R. China
2School of Computing and Information Sciences, Florida International University, Miami, FL
3School of Computer Science, Nanjing University of Posts and Telecommunications, Jiangsu, P.R. China

Tóm tắt

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

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Tài liệu tham khảo

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