Big Data in Natural Disaster Management: A Review
Manzhu Yu,Chaowei Yang,Yun Li +2 more
- Vol. 8, Iss: 5, pp 165
Reads0
Chats0
TLDR
This paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.Abstract:
Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.read more
Citations
More filters
Journal ArticleDOI
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
TL;DR: This survey examines the potential and benefits of data-driven research in EWM, gives a synopsis of key concepts and approaches in BigData andML, provides a systematic review of current applications, and discusses major issues and challenges to recommend future research directions.
Journal ArticleDOI
Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management
TL;DR: A vision for a Disaster City Digital Twin paradigm that can enable interdisciplinary convergence in the field of crisis informatics and information and communication technology in disaster management and integrate artificial intelligence algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders is presented.
Journal ArticleDOI
OCHSA: Designing Energy-Efficient Lifetime-Aware Leisure Degree Adaptive Routing Protocol with Optimal Cluster Head Selection for 5G Communication Network Disaster Management
S. Raja,J. Logeshwaran,S. Venkatasubramanian,M. Jayalakshmi,N. Rajeswari,N. G. Olaiya,Wubishet Degife Mammo +6 more
TL;DR: This study focuses on optimal cluster head selection using the binary flower pollination optimization algorithm by designing an energy-efficient lifetime-aware leisure degree adaptive routing protocol named OptCH_L-LDAR, which achieves 96% of energy efficiency, 89% of lifetime, 97% of outage probability, and 98% of spectral efficiency.
Journal ArticleDOI
Applications of artificial intelligence for disaster management
TL;DR: It is found that the majority of AI applications focus on the disaster response phase, and challenges to inspire the professional community to advance AI techniques for addressing them in future research are identified.
Journal ArticleDOI
Computational Socioeconomics
Jian Gao,Yi-Cheng Zhang,Tao Zhou +2 more
TL;DR: In this article, the authors make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events.
References
More filters
Journal ArticleDOI
At Risk: Natural Hazards, People's Vulnerability, and Disasters.
TL;DR: The authors argue that the social, political and economic environment is as much a cause of disasters as the natural environment and that the concept of vulnerability is central to an understanding of disasters and their prevention or mitigation, exploring the extent and ways in which people gain access to resources.
Journal ArticleDOI
Genetic Algorithms and Machine Learning
TL;DR: There is no a priori reason why machine learning must borrow from nature, but many machine learning systems now borrow heavily from current thinking in cognitive science, and rekindled interest in neural networks and connectionism is evidence of serious mechanistic and philosophical currents running through the field.
Journal ArticleDOI
The rise of big data on cloud computing
Ibrahim Abaker Targio Hashem,Ibrar Yaqoob,Nor Badrul Anuar,Salimah Binti Mokhtar,Abdullah Gani,Samee U. Khan +5 more
TL;DR: The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced, and research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance.
Journal ArticleDOI
Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Journal ArticleDOI
Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data
TL;DR: Experimental results show that two different approaches have unique advantages and disadvantages in this classification application of multisource remote sensing and geographic data.