Research Article
Data-Driven Disaster Management in a Smart City
@INPROCEEDINGS{10.1007/978-3-030-97603-3_9, author={Sandra P. Gon\`{e}alves and Joao C Ferreira and Ana Madureira}, title={Data-Driven Disaster Management in a Smart City}, proceedings={Intelligent Transport Systems. 5th EAI International Conference, INTSYS 2021, Virtual Event, November 24-26, 2021, Proceedings}, proceedings_a={INTSYS}, year={2022}, month={3}, keywords={Disaster management Data mining Machine learning Smart city}, doi={10.1007/978-3-030-97603-3_9} }- Sandra P. Gonçalves
Joao C Ferreira
Ana Madureira
Year: 2022
Data-Driven Disaster Management in a Smart City
INTSYS
Springer
DOI: 10.1007/978-3-030-97603-3_9
Abstract
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%.
