The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Big Data in Natural Disaster Management: A Review
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Genetic Algorithms and Machine Learning
Goldberg, D. E., & Holland, J. H. (1988) · Machine Learning · doi:10.1023/a:1022602019183 · Cited by 3,193
The rise of big data on cloud computing: Review and open research issues
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015) · Information Systems · doi:10.1016/j.is.2014.07.006 · Cited by 2,656
The Death Toll from Natural Disasters: The Role of Income, Geography, and Institutions
Kahn, M. E. (2005) · Review of Economics and Statistics · doi:10.1162/0034653053970339 · Cited by 1,393
Help from the Sky: Leveraging UAVs for Disaster Management
Erdelj, M., Natalizio, E., Chowdhury, K. R., & Akyildiz, I. F. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.11 · Cited by 996
Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1990) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.1990.572944 · Cited by 945
Crowdsourcing geographic information for disaster response: a research frontier
Goodchild, M. F., & Glennon, J. A. (2010) · International Journal of Digital Earth · doi:10.1080/17538941003759255 · Cited by 909
Global Seasonal forecast system version 5 (GloSea5): a high‐resolution seasonal forecast system
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., et al. (2015) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2396 · Cited by 727
Shao, Y., & Lunetta, R. S. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2012.04.001 · Cited by 627
Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (2014) · Routledge eBooks · doi:10.4324/9780203714775 · Cited by 618
Cited by
Applications of artificial intelligence for disaster management
Sun, W., Bocchini, P., & Davison, B. D. (2020) · Natural Hazards · doi:10.1007/s11069-020-04124-3 · Cited by 491
Fan, C., Zhang, C., Yahja, A., & Mostafavi, A. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.102049 · Cited by 434
Khan, A., Gupta, S., & Gupta, S. K. (2020) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2020.101642 · Cited by 357
Machine Learning in Disaster Management: Recent Developments in Methods and Applications
Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020020 · Cited by 319
The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review
Meechang, K., Leelawat, N., Tang, J., Kodaka, A., & Chintanapakdee, C. (2020) · Engineering Journal · doi:10.4186/ej.2020.24.4.111 · Cited by 65
“A systematic review of big data in disaster management analyzed roles of technology for effective solutions [16].”
Background
Metadata
- Authors
- Yu, M., Yang, C., & Li, Y.
- Venue
- Geosciences
- Publisher
- MDPI AG
- Published
- 2018-05-05
- Volume
- 8
- Issue
- 5
- Pages
- 165
- ISSN
- 2076-3263 (ISSN-L)
- OpenAlex
- W2801081735
- Cited by
- 447
- References
- 89
- FWCI
- 71.76
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Big Data in Natural Disaster Management: A Review. https://app.cochat.ai/papers/10.3390/geosciences8050165
- The paper
- Yu, M., Yang, C., & Li, Y. (2018). Big Data in Natural Disaster Management: A Review. Geosciences. https://doi.org/10.3390/geosciences8050165
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.