Comparação de Classificadores de Aprendizado de Máquina para Modelagem de Distribuição de Espécies: um estudo de caso na Bacia Amazônica
Resumo
Na Ecologia, a Modelagem de Distribuição de Espécies é utilizada para analisar a influência de variáveis atmosféricas e meteorológicas na ocorrência de espécies. Nas últimas décadas, Classificadores de Aprendizado de Máquina foram aplicados com sucesso. Dessa forma, este artigo buscou comparar sete técnicas de Aprendizado de Máquina para avaliar sua viabilidade. Essas foram aplicadas para um estudo de caso sobre pássaros na região central da Bacia Amazônica próxima a Manaus (AM), com dados do projeto GoAmazon 2014/15. O classificador com melhor ROC-AUC foi o Gradient Boosting com 94%. O modelo de Máxima Entropia teve a melhor revocação (85%). O modelo Florestas Aleatórias teve a melhor ponderação entre as métricas.
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Breiman, L. (2001). Random forests. Machine Learning, 45:5–32.
Carter, S., van Rees, C. B., Hand, B. K., Muhlfeld, C. C., Luikart, G., and Kimball, J. S. (2021). Testing a generalizable machine learning workflow for aquatic invasive species on rainbow trout (oncorhynchus mykiss) in northwest montana. Frontiers in Big Data, 4.
Derville, S., Torres, L. G., Iovan, C., and Garrigue, C. (2018). Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Diversity and Distributions, 24:1657–1673.
Effrosynidis, D., Tsikliras, A., Arampatzis, A., and Sylaios, G. (2020). Species distribution modelling via feature engineering and machine learning for pelagic fishes in the mediterranean sea. Applied Sciences, 10(24).
Elith, J. and Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. The Annual Review of Ecology, Evolution and Systematics, 40:677–697.
Fern, R. R., Morrison, M. L., Grant, W. E., Wang, H., and Campbell, T. A. (2020). Modeling the influence of livestock grazing pressure on grassland bird distributions. Ecological Processes, 9(42).
Georgian, S., Morgan, L., and Wagner, D. (2021). The modeled distribution of corals and sponges surrounding the salas y gómez and nazca ridges with implications for high seas conservation. Peer J, 9.
Ghareghan, F., Ghanbarian, G., Pourghasemi, H. R., and Safaeian, R. (2020). Prediction of habitat suitability of morina persica l. species using artificial intelligence techniques. Ecological Indicators, 112.
Hegel, T. M., Cushman, A., Evans, J., and Huetmann, F. (2010). Spatial Complexity, Informatics and Wildlife Conservation, chapter Current State of the Art for Statistical Modelling of Species Distributions. Springer.
Hernandez, P. A., Graham, C. H., Master, L. L., and Albert, D. L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5):773–785.
Hutchinson, G. E. (1991). Population studies: Animal ecology and demography. Bulletin of Mathematical Biology, 53(1-2):193–213.
Johnson, R., Chawla, N., and Hellmann, J. (2012). Species distribution modeling and prediction: A class imbalance problem. pages 9–16.
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A. F. d., Schumacher, C., Wang, J., Biscaro, T., Brito, J., Calheiros, A., et al. (2017). The green ocean amazon experiment (goamazon2014/5) observes pollution affecting gases, aerosols, clouds, and rainfall over the rain forest. Bulletin of the American Meteorological Society, 98(5):981–997.
Mateo, R. G., Vanderpoorten, A., Muñoz, J., Laenen, B., and Désamoré, A. (2013). Modeling species distributions from heterogeneous data for the biogeographic regionalization of the european bryophyte flora. PLoS One, 8(2):e55648.
Miyaji, R. O., Almeida, F. V., Bauer, L. O., Ferrari, V., Corrêa, P. L. P., Rizzo, L. V., and Prakash, G. (2021). Spatial interpolation of air pollutant and meteorological variables in central amazonia. Data, 6(12).
Nurhussen, A., Atzberger, C., and Zewdia, W. (2021). Species distribution modelling performance and its implication for sentinel-2-based prediction of invasive prosopis juliflora in lower awash river basin, ethiopia. Ecological Processes, 10(18).
Pinaya, J. and Corrêa, P. (2014). Metodologia para definição das atividades do processo de modelagem de distribuição de espécies. In Anais do V Workshop de Computação Aplicada a Gestão do Meio Ambiente e Recursos Naturais, pages 45–54, Porto Alegre, RS, Brasil.
Rahman, M. S., Pietong, C., Zafar, S., Ekalasananan, T., Paul, R. E., Haque, U., Rocklöv, J., and Overgaard, H. J. (2021). Mapping the spatial distribution of the dengue vector aedes aegypti and predicting its abundance in northeastern thailand using machine-learning approach. One Health, 13.
The Imbalanced-learn Developers (2021). Imbalanced-learn documentation. https://imbalanced-learn.org/stable/. Acesso em: 18/08/2022.
Publicado
06/08/2023
Como Citar
MIYAJI, Renato O.; ALMEIDA, Felipe V. de; CORRÊA, Pedro L. P.; RIZZO, Luciana V.; CALHEIROS, Alan; TEIXEIRA, Márcio.
Comparação de Classificadores de Aprendizado de Máquina para Modelagem de Distribuição de Espécies: um estudo de caso na Bacia Amazônica. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 14. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 41-50.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2023.229365.
