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Machine Learning to Estimate the Floating Population in Florianopolis

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
Denilton Luiz Darold, Carlos Roberto Da Rolt, Andrea Sabbioni
10.5120/ijca2020920812

Denilton Luiz Darold, Carlos Roberto Da Rolt and Andrea Sabbioni. Machine Learning to Estimate the Floating Population in Florianopolis. International Journal of Computer Applications 175(27):1-6, October 2020. BibTeX

@article{10.5120/ijca2020920812,
	author = {Denilton Luiz Darold and Carlos Roberto Da Rolt and Andrea Sabbioni},
	title = {Machine Learning to Estimate the Floating Population in Florianopolis},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2020},
	volume = {175},
	number = {27},
	month = {Oct},
	year = {2020},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume175/number27/31620-2020920812},
	doi = {10.5120/ijca2020920812},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Touristic cities experience high fluctuation in their population, especially during the summer season. For many cities and countries, tourism plays a vital role in the economy, generating revenue and creating jobs. However, this so welcome economic boost comes along with an overload on public services, once the population increases dramatically in the high season. Therefore, an accurate method to predict the touristic demand is critical to provide the city administrators the necessary information for proper planning. Moreover, the private sector depends on demand forecasting to invest and maximize its profits. The most used methods currently rely on surveys and traditional indicators like the hotel

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Keywords

Floating population, seasonality, tourism measurement, machine learning