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Reseach Article

Machine Learning to Estimate the Floating Population in Florianopolis

by Denilton Luiz Darold, Carlos Roberto Da Rolt, Andrea Sabbioni
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 27
Year of Publication: 2020
Authors: Denilton Luiz Darold, Carlos Roberto Da Rolt, Andrea Sabbioni
10.5120/ijca2020920812

Denilton Luiz Darold, Carlos Roberto Da Rolt, Andrea Sabbioni . Machine Learning to Estimate the Floating Population in Florianopolis. International Journal of Computer Applications. 175, 27 ( Oct 2020), 1-6. DOI=10.5120/ijca2020920812

@article{ 10.5120/ijca2020920812,
author = { Denilton Luiz Darold, Carlos Roberto Da Rolt, Andrea Sabbioni },
title = { Machine Learning to Estimate the Floating Population in Florianopolis },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2020 },
volume = { 175 },
number = { 27 },
month = { Oct },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number27/31620-2020920812/ },
doi = { 10.5120/ijca2020920812 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:39:33.800487+05:30
%A Denilton Luiz Darold
%A Carlos Roberto Da Rolt
%A Andrea Sabbioni
%T Machine Learning to Estimate the Floating Population in Florianopolis
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 27
%P 1-6
%D 2020
%I Foundation of Computer Science (FCS), NY, 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

References
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Index Terms

Computer Science
Information Sciences

Keywords

Floating population seasonality tourism measurement machine learning