Silva, Felipe Roberto da, Câmara, Samuel Façanha, Pinto, Francisco Roberto, Costa, Francisco José da, Freitas, Leonardo Martins de, Oliveira Júnior, José Gilmar Cavalcante de, De Paula, Thiago Matheus and Soares, Marcelo de Oliveira ORCID: (2023) Machine learning application to assess deforestation and wildfire levels in protected areas with tourism management. Journal for Nature Conservation, 74 . p. 126435. DOI

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This study aims to identify the influence of management plans, management boards and tourism management on the relationship between performance indicators in the management of protected areas and degradation processes. To understand these relationships, 283 protected areas (PAs) in Brazil were analyzed. The first stage of the research used classification models based on machine learning algorithms, which revealed that predictive variables were a promising way to assess PAs vulnerability to deforestation and wildfire, giving decision-makers an 87.5% and 72.8% chance, respectively, of correctly identifying the PAs more susceptible to these threats. The predictive variables more relevant to deforestation were biome, area, and tourism management, while for wildfire, governance and PA type were the most relevant. Predictive variables were also a promising way to assess PAs management, giving decision-makers a 79.7% and 78.1% chance, respectively, to correctly identify the PAs with higher levels of effectiveness and governance. In addition, in the second stage, to empirically reinforce the models, multivariate analyses were performed, through which it was possible to confirm that deforestation levels are significantly higher in areas of sustainable use than in fully protected areas and determine how the positive interaction with tourism management contributes to the reduction in deforestation records and improves effectiveness. Therefore, it is understood that tourism management can strongly influence the sustainability of natural resources, and it is of utmost importance to generate tourism management policies with potential value generation for natural spaces.

Document Type: Article
Programme Area: PA2
Research affiliation: Ecology > Reef Systems
Refereed: Yes
Open Access Journal?: No
ISSN: 16171381
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Date Deposited: 26 Jun 2023 12:12
Last Modified: 26 Mar 2024 13:31

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