Analysis of Disaster’s and its Management Using Big Data Analytics

Authors

  • Nirbhay Mishra CSE /Assistant Professor, JIS University, Kolkata, India
  • Dharmpal Singh CSE /Assistant Professor, JIS University, Kolkata, India
  • Amit Majumdar CSE /Assistant Professor, JIS University, Kolkata, India
  • Shinjini Nag CSE /Assistant Professor, JIS University, Kolkata, India
  • Manish Gupta CSE /Student, JIS University, Kolkata, India
  • Radha krishna Jana CSE /Assistant Professor, JIS University, Kolkata, India

Keywords:

Disaster, Earthquake, drought, Covid19, epidemic, AI, big data

Abstract

Disaster means a calamity which ruins the human civilization time to time at a bigger platform in many ways, it may be natural or it may be manmade, now days we are observing that almost all part of the world is suffering from CORONA a deadliest virus seems to be made by china as report and all countries corps are assuming or predicting the exact reason from year 2019 to till date. Above problem is the example of manmade disaster but apart from this we know the different types of disaster and their consequences in terms of loss in various ways. Its shows bad impact in almost all part of life from bottom to top as it influences life on all three platform i.e. socially, economically, and politically as a result peoples are start dying in lack of food and all various things which they need to survive their life on daily basis. Some of them will not only lose their family they will lose their shelter also by keeping all scenarios, Here we will try to build a completely different model which not only save the human life rather it will provides strength economically in such way that if happened such things then we all together can face any kind of problem and find a way to get out of it and also predict the future disaster in advance by applying Machine learning methods

References

[1] Potter, S.H., Becker, J.S., Johnston, D.M. and Rossiter K.P. An overview of the impacts of the 2010-2011 Canterbury earthquakes. International Journal of Disaster Risk Reduction, 14, pp.6-14, 2015.

[2] Rufat, S., Tate, E., Burton, C.G. and Maroof A.S. Social vulnerability to floods: Review of case studies and implications for measurement. International journal of disaster risk reduction, 14, pp.470-486, 2015. https://doi.org/10.1016/j.ijdrr.2015.09.013

[3]Whittaker, J., McLennan, B. and Handmer J. A review of informal volunteerism in emergencies and disasters: Definition, opportunities and challenges. International journal of disaster risk reduction, 13, pp.358-368, 2015. https://doi.org/10.1016/j.ijdrr.2015.07.010

[4]Hajito, K.W., Gesesew, H.A., Bayu, N.B. and Tsehay Y.E. Community awareness and perception on hazards in Southwest Ethiopia: A cross-sectional study. International journal of disaster risk reduction, 13, pp.350-357, 2015. https://doi.org/10.1016/j.ijdrr.2015.07.012

[5]Boonmee, C., Arimura, M. and Asada, T., 2017. Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, pp.485-498, 2017. https://doi.org/10.1016/j.ijdrr.2017.01.017

[6]Skryabina, E., Reedy, G., Amlot, R., Jaye, P. and Riley, P., 2017. What is the value of health emergency preparedness exercises? A scoping review study. International journal of disasterrisk reduction, 21, pp.274-283. https://doi.org/10.1016/j.ijdrr.2016.12.010

[7]Udmale, P., Ichikawa, Y., Manandhar, S., Ishidaira, H. and Kiem, A.S., 2014. Farmers? perception of drought impacts, local adaptation and administrative mitigation measures in Maharashtra State, India. International Journal of Disaster Risk Reduction, 10, pp.250-269, 2014. https://doi.org/10.1016/j.ijdrr.2014.09.011

[8]Hoque, M.A.A., Phinn, S. and Roelfsema, C., 2017. A systematic review of tropical cyclone disaster management research using remote sensing and spatial analysis. Ocean & Coastal Management, 146, pp.109-120, 2017. https://doi.org/10.1016/j.ocecoaman.2017.07.001

[9] Slack, M.D., Prasad, R.O., Bakker, A. and Boysan, F., 2000. Advances in cyclone modelling using unstructured grids. Chemical Engineering Research and Design, Vol.8, Issue.78, pp.1098-1104, 2000.

[10] Myers, C.G., Oster, J.L., Sharp, W.D., Bennartz, R., Kelley, N.P., Covey, A.K. and Breitenbach, S.F., 2015. Northeast Indian stalagmite records Pacific decadal climate change: Implications for moisture transport and drought in India. Geophysical Research Letters, Vol.42, Issue.10, pp.4124-4132, 2015. https://doi.org/10.1002/2015GL063826

[11] Ramalanjaona, G., 2011. Impact of 2004 tsunami in the islands of Indian ocean: Lessons learned. Emergency medicine international, 2011. https://doi.org/10.1155/2011/920813

[12] Vijay, V. and Raghava, S.V., 2021. Pattern of Injuries in Railway Fatalities at Bowring and Lady Curzon Hospital, Bangalore. Indian Journal of Forensic Medicine & Toxicology, 15(3), pp.586-589, 2021.

[13] Deshpande, P., 2014. Road safety and accident prevention in India: a review. International journal of advanced engineering technology, Vol.5, Issue.2, pp.64-68, 2014.

[14] Hannah Ritchie and Pablo Rosado. “Natural Disasters” Our World in Data. December 7, 2022.

[15] Annual Heat Wave Index in the United States, 2022.

Downloads

Published

2026-01-19

How to Cite

[1]
N. Mishra, D. Singh, A. Majumdar, S. Nag, M. Gupta, and R. krishna Jana, “Analysis of Disaster’s and its Management Using Big Data Analytics”, Int. J. Comp. Sci. Eng., vol. 11, no. 1, pp. 1–11, Jan. 2026.

Issue

Vol. 11 No. 1 (2023): Special Issue-1 Nov Editon

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.