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Gonçalves, S. P:, Ferreira, J. C. & Madureira, A. (2021). Data-driven disaster management in a smart city. In Intelligent Transport Systems. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. (pp. 113-132).: Springer Cham.

Sandra et al.,  "Data-driven disaster management in a smart city", in Intelligent Transport Systems. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer Cham, 2021, vol. 426, pp. 113-132

@inproceedings{sandra2021_1714854956600,
	author = "Gonçalves, S. P: and Ferreira, J. C. and Madureira, A.",
	title = "Data-driven disaster management in a smart city",
	booktitle = "Intelligent Transport Systems. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering",
	year = "2021",
	editor = "",
	volume = "426",
	number = "",
	series = "",
	pages = "113-132",
	publisher = "Springer Cham",
	address = "",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-030-97603-3"
}

TY  - CPAPER
TI  - Data-driven disaster management in a smart city
T2  - Intelligent Transport Systems. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
VL  - 426
AU  - Gonçalves, S. P:
AU  - Ferreira, J. C.
AU  - Madureira, A.
PY  - 2021
SP  - 113-132
SN  - 1867-8211
UR  - https://link.springer.com/book/10.1007/978-3-030-97603-3
AB  - Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. The advances in IT and Big Data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. This research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.
ER  -