TY - CHAP

T1 - Social Media-Based Intelligence for Disaster Response and Management in Smart Cities

AU - Khatoon, Shaheen

AU - Asif, Amna

AU - Hasan, Md Maruf

AU - Alshamari, Majed

N1 - 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.

PY - 2022/1/9

Y1 - 2022/1/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85122649682&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-84459-2_11

DO - 10.1007/978-3-030-84459-2_11

M3 - Chapter

AN - SCOPUS:85122649682

SN - 9783030844585

T3 - Springer Optimization and Its Applications

SP - 211

EP - 235

BT - Springer Optimization and Its Applications

PB - Springer

CY - London

ER -