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Social Media-Based Intelligence for Disaster Response and Management in Smart Cities

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

  • Shaheen Khatoon
  • Amna Asif
  • Md Maruf Hasan
  • Majed Alshamari
Publication date9/01/2022
Host publicationSpringer Optimization and Its Applications
Place of PublicationLondon
Number of pages25
ISBN (electronic)9783030844592
ISBN (print)9783030844585
<mark>Original language</mark>English

Publication series

NameSpringer Optimization and Its Applications
ISSN (Print)1931-6828
ISSN (electronic)1931-6836


This chapter highlights the key challenges of our ongoing project in developing an information technology solution for emergency response and management in smart cities. We aim to develop a cloud-based big data framework that will enable us to utilize heterogeneous data sources and sophisticated machine learning techniques to gather, process, and integrate information intelligently to support emergency response to any disaster or crisis rapidly. After identifying the right data sources, we turn our attentions into investigating suitable techniques that can be utilized in disaster-event detection as well as extraction and representation of useful features related to the disaster. We also outline our approach in analysis and integration of disaster-related knowledge with the help of a disaster ontology. Our ultimate goal is to display and disseminate actionable information to the decision-makers in the format most appropriate for carrying out emergency response and coordination efficiently. We developed a dashboard-like interface to facilitate such goal. For any disaster or emergency, the heterogeneous nature (texts, image, audio, and videos) and sheer volume of data instantly available on the social media platforms necessitate fast and automated processing (including integration and fusion of information originating from disparate sources). This chapter highlights our ongoing research in addressing such challenges in an automated fashion using state-of-the-art artificial intelligence and machine learning techniques suitable for processing multimodal social-media data. Our research contributions will eventually facilitate building a comprehensive disaster management framework and system that may streamline emergency response operations in the smart cities.

Bibliographic note

Funding Information: The authors extend their appreciation to the Deanship of Scientific Research (DSR) and College of Computer Science and Information Technology at King Faisal University, Saudi Arabia, for facilitating this research work. Funding Information: Funding Statement The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, for funding this research work through project number 523. Publisher Copyright: © 2022, Springer Nature Switzerland AG.