Assessing Long-term Impacts of Disaster Using Predictive Data Analytics for Effective Decision Support
Main Article Content
Abstract
Disaster is a big issue that seriously disrupts and affects the community or society. The impact of a disaster causes a short and long period of time. To analyze the impacts of disasters there are lots of available related datasets. Data analytics methods have the potential to assess the impacts of different types of disasters. Collectively data analytics and machine learning techniques play an important role in transforming and being able to make decisions about our social, economic, mental, and psychological things. The objective of this paper is to assess the impacts of disasters from immediate term to long-term, provide crucial help to the emergency management workforce, and policy decisions making based on the latest available datasets. With the help of the various data agencies, extraction of information and activities carried out, we can determine the effects on disaster victims, their community and impacts on society in general. The analysis provides the statistics that can guide our emergency service about the status of facilities that can further support the survivors, and other related information. Detailed assessment i.e., structural survey and hazard mapping provide specific information about reconstruction and mitigation to monitor the situation, needs of the victims, and supporting entities. The assessment is based on the type of disaster that happened and its impact after a few years.
In the current technological advancement of data analytics and machine learning algorithms, the prediction of the long-term effects of a disaster can be performed. Analyzing the impacts over a long period of time is also dependent on the growth of actively cared datasets gathering bodies like agencies, government, NGOs, media, etc., where prediction of short-term and long-term impacts is dependent on the available datasets. Available datasets are preprocessed using data analytics tools and implementation of training and testing for the purpose of predictions and recommendations. As a huge amount of data sets are available through different sources the classification of the datasets can also be performed resulting fast and accurate processing. Model validation techniques play an important role to check the validation, test result, and related outcomes. In this paper advanced machine learning and data analytics tools i.e., XG boost, modified SVM, and modified RF are used for better prediction. The analysis of the short-term effects of disasters has already been suggested and recommended by the various conventional approaches. Here the focus is to analyze and detect the long-term effects of a disaster along with recommendations and models preparing for good decision-making. Therefore, planning should be focused on assessing the impacts from short-term to long-term. The findings of the paper would be helpful to the agencies, local & national authorities, and the government by recommending action plans and their future effects for a longer period in case of disaster.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
C. Iglesias, A. Favenza and Ã. Carrera, "A Big Data Reference Architecture for Emergency Management", Information, vol. 11, no. 12, p. 569, 2020. Available: 10.3390/info11120569.
V. Nandhini, Dr. M. S. Geetha Devasena, Predictive Analytics for Climate Change Detection and Disease Diagnosis, 5th International Conference on Advanced Computing & Communication Systems (ICACCS) 978-1- 5386-9533-3/19, 2019 IEEE
Akter S , Wamba S F . Big data and disaster management: a systematic review and agenda for future research[J]. Annals of Operations Research, 2017
S. Manyena, E. Mavhura, C. Muzenda and E. Mabaso, "Disaster risk reduction legislations: Is there a move from events to processes?", Global Environmental Change, vol. 23, no. 6, pp. 1786-1794, 2013. Available: 10.1016/j.gloenvcha. 2013. 07.027.
Ho, D.H. and Lee, Y. (2021) “Big Data Analytics framework for predictive analytics using public data with Privacy preserving,†2021 IEEE International Conference on Big Data (Big Data) [Preprint]. Available at: doi.org/10.1109/bigdata 52589.2021. 9671997.
H. Lee and H. Chen, "Implementing the Sendai Framework for disaster risk reduction 2015–2030: Disaster governance strategies for persons with disabilities in Taiwan", International Journal of Disaster Risk Reduction, vol. 41, p. 101284, 2019. Available: 10.1016/j.ijdrr.2019.101284.
C. Lomnitz, "IFRC: World Disasters Report 2014: focus on Culture and Risk", Natural Hazards, vol. 77, no. 2, pp. 1393-1394, 2015. Available: 10.1007/s11069-015-1655-4.
Avery, A. (2016) “After the disclosure: Measuring the short-term and long-term impacts of data breaches on Firm performance,†SSRN Electronic Journal [Preprint]. Available at: https://doi.org/10.2139/ssrn.3674559.
Fry, J., & Binner, J. M. (2016). Elementary modelling and behavioral analysis for emergency evacuations using social media. European Journal of Operational Research, 249(3), 1014–1023. doi.org/10.1016/j.ejor.2015.05.049.
M. Yu, C. Yang and Y. Li, "Big Data in Natural Disaster Management: A Review", Geosciences, vol. 8, no. 5, p. 165, 2018. Available: 10.3390/ geosciences 8050165.
Tan, S.B., Waters, M.C. and Arcaya, M.C. (2022) “Analyzing the long-term impact of post-disaster relocation and implications for disaster recovery policy,†International Journal of Disaster Risk Reduction, 70, p. 102765. Available at: doi.org/10.1016/j.ijdrr. 2021.102765.
Freeman, J.D.; Blacker, B.; Hatt, G.; Tan, S.; Ratcliff, J.; Woolf, T.B.; Tower, C.; Barnett, D.J. Use of big data and information and communications technology in disasters: an integrative review. Disaster Med. Public Health Prep. 2019, 13, 353–367.
Chaudhuri, N. and Bose, I. (2020) “Exploring the role of deep neural networks for post-disaster decision support,†Decision Support Systems, 130, p. 113234. Available at: doi.org/10.1016/j.dss. 2019.113234.
Aggarwal, L. and Goswami, P. (2022) “Predictive big data analytics and privacy based decision support system,†Trust, Security and Privacy for Big Data, pp. 89–111. Available at: https://doi.org/10.1201/9781003194538-5.
Shrivatava, A. (2017) “A collective study of machine learning (ML) algorithms with Big Data Analytics (BDA) for Healthcare Analytics (HCA),†International Journal of Computer Trends and Technology, 47(3), pp. 156– 160. Available at: doi.org/10.14445/22312803/ijctt-v47p122.
Akila1, A., Parameswari, R. and Jayakumari, C. (2022) “Big Data in Healthcare: Management, analysis, and future prospects,†Handbook of Intelligent Healthcare Analytics, pp. 309–326. Available at: doi.org/10.1002/ 9781119792550.ch14.
Mishra, S.K. and Rahamatkar, S. (2021) “Role of predictive data analytics to assess long term impacts of disaster,†2021 5th International Conference on Information Systems and Computer Networks (ISCON). Available at: doi.org/10.1109/iscon52037. 2021. 9702423.
Hossein Hassani , Xu Huang and Emmanuel Silva Review Big Data and Climate Change, MDPI, Big Data Cogn. Comput. 2019, 3, 12; doi:10.3390/bdcc3010012
Cavdur, F., Sebatli-Saglam, A. and Kose-Kucuk, M. (2020) “A spreadsheet-based decision support tool for temporary-disaster-response facilities allocation,†Safety Science, 124, p. 104581. Available at: doi.org/10.1016/j.ssci. 2019. 104581.
Wen, R. and Zhang, K. (2022) “Research on Automated Classification Method of network attacking based on Gradient Boosting Decision tree,†2022 International Conference on Machine Learning and Knowledge Engineering (MLKE) [Preprint]. Available at: doi.org/10.1109/mlke55170.2022.00019.
M. Sarabia et al., "The challenges of impact evaluation: Attempting to measure the effectiveness of community-based disaster risk management", International Journal of Disaster Risk Reduction, vol. 49, p. 101732, 2020. Available: 10.1016/j.ijdrr. 2020.101732.
The Intergovernmental Panel on Climate Change (no date) IPCC. Available at: https://www.ipcc.ch/.
Meteorological & Oceanographic Satellite Data Archival Centre (no date) Meteorological & Oceanographic Satellite Data Archival Centre | Space Applications Centre, ISRO. Available at: http://www.mosdac.gov.in/.
National Disaster Management Authority (no date) Annual Reports | NDMA, GoI. Available at: http://www.ndma.gov.in/index.php /Resources/AnnualReports.
Rania Rizki Arinta, Andi W.R. Emanuel, Natural Disaster Application on Big Data and Machine Learning: A Review, IEEE Nov,2019 DOI: 10.1109/ICITISEE48480.2019.9003984