Figure 2.4 Changes from various impacts from natural disaster.
Figure 2.5 Economic damage changes a prediction analysis.
Figure 2.6 Boxplot view of natural disaster on various entity.
References
1. Baboo, S.S., Baboo, S.S., Shereef, I.K., An Efficient Weather Forecasting System Using Artificial Neural Network. Int. J. Environ. Sci. Dev., 1, 321–326, 2010, https://doi.org/10.7763/ijesd.2010.v1.63.
2. Cognitive Tasks and Learning, n.d. SpringerReference, https://doi.org/10.1007/springerreference_226188.
3. Li, D. and Yu, D., Deep Learning: Methods and Applications. Found. Trends Signal Process., 7, 3–4, 197–387, 2014.
4. Grinsted, A., Ditlevsen, P., Christensen, J.H., Normalized US Hurricane Damage Estimates Using Area of Total Destruction. Proceedings of the National Academy of Sciences of the United States of America, vol. 116, pp. 23942–46, 20191900-2018.
5. Anand, J. et al., Efficient Security for Desktop Data Grid using Fault Resilient Content Distribution. Int. J. Eng. Res. Ind. Appl., 2, 7, 301–313, 20092009.
6. Anand, J. et al., Efficient Data Storage in Desktop Data-Grid Computing using Real-Time Parameters. Int. J. Comput. Sci. Technol., 2, 3, 392–397, 2011.
7. Kuswanto, and Kuswanto, Bayesian Model Averaging with Markov Chain Monte Carlo for Calibrating Temperature Forecast from Combination of Time Series Models. J. Math. Stat., 9, 349–356, 2013, https://doi.org/10.3844/jmssp.2013.349.356.
8. Minsley, B.J., A Trans-Dimensional Bayesian Markov Chain Monte Carlo Algorithm for Model Assessment Using Frequency-Domain Electromagnetic Data. Geophys. J. Int., 187, 252–272 2011, https://doi.org/10.1111/j.1365-246x.2011.05165.x.
9. Padli, J., Habibullah, M.S., Baharom, A.H., The Impact of Human Development on Natural Disaster Fatalities and Damage: Panel Data Evidence. Econ. Res.-Ekon. Istraž., 7, 1–17, 2018, https://doi.org/10.1080/1331677x.2018.1504689.
10. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Shyu, M.-L., Chen, S.-C., Iyengar, S.S., A Survey on Deep Learning. ACM Comput. Surv., 22, 1–23, 2019, https://doi.org/10.1145/3234150.
11. R. Stuart, J., Stuart, J.R., Norvig, P., Davis, E., Artificial Intelligence: A Modern Approach, Prentice Hall, Springer, New Jersey, 2010.
12. Vista, A., Awwal, N., Care, E., Sequential Actions as Markers of Behavioural and Cognitive Processes: Extracting Empirical Pathways from Data Streams of Complex Tasks. Comput. Educ., 92–93, 15–36, 2016, https://doi.org/10.1016/j.compedu.2015.10.009.
13. Wollsen, M.G. and Jørgensen, B.N., Improved Local Weather Forecasts Using Artificial Neural Networks. Distributed Computing and Artificial Intelligence, 12th International Conference, 2015, https://doi.org/10.1007/978-3-319-19638-1_9.
14. On Sequential Spectral Analysis of Amplitude-Modulated Time Series, in: Sequential Analysis.
15. Habermann, A.N., Engineering Large Knowledge-Based Systems. Data Knowl. Eng., 15, 105–117, 1990, https://doi.org/10.1016/0169-023x(90)90007-z.
16. Kellett, J.M., Winstanley, G., Boardman, J.T., A Methodology for Knowledge Engineering Using an Interactive Graphical Tool for Knowledge Modelling. Artif. Intell. Eng., 4, 92–102, 1989, https://doi.org/10.1016/0954-1810(89)90004-6.
17. Knowledge Management and Knowledge Engineering. Knowl. Manage., 152, 2001, https://doi.org/10.1201/9781420041125.ch1.
18. Mohamudally, N., Introductory Chapter: Time Series Analysis (TSA) for Anomaly Detection in IoT, in: Time Series Analysis and Applications, 2018.
19. Motta, E., THE KNOWLEDGE MODELING PARADIGM IN KNOWLEDGE ENGINEERING, in: Handbook of Software Engineering and Knowledge Engineering, 2001.
20. Niharika, M., Deepa, N., Devi., T., Image segmentation and detection for healthcare data in deep learning, in: Test Engineering and Management, vol. 81, pp. 5402–5407, 2019.
21. Vaishnavi, R. et al., Efficient Security for Desktop Data Grid using Cryptographic Protocol. Proceedings of IEEE International Conference on Control, Automation, Communication and Energy Conservation, vol. 1, pp. 305–211, 2009.
22. Śmietanowski, M., Nonlinear Parameters Estimation from Sequential Short Time Data Series. Auton. Neurosci., 90, 158–166, 2001, https://doi.org/10.1016/s1566-0702(01)00283-1.
23. Zhang, Y., Zhong, M., Geng, N., Jiang., Y., Forecasting Electric Vehicles Sales with Univariate and Multivariate Time Series Models: The Case of China. PloS One, 12, 5, e0176729, 2017.
24. Gunn, S.W.A., The Scientific Basis of Disaster Management. Disaster Prev. Manag.: An Int. J., 1, 446 1992, https://doi.org/10.1108/09653569210018672.
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