Prediction of anurans occupation using environmental features and Autoencoder models
Abstract
We present a model for predicting geographic distribution of anurans based on Autoencoder. The prediction problem is treated as an One-Class clas- sification task, in which each sample is represented by its geographic coordinates, temporal and correlated meteorological variables. Our model shows a high degree of accuracy when used to predict the occurrence of the Bufo Americanus species in southern Canada. The proposed method is computationally inexpensive and can be coupled to sensor networks for environmental monitoring.
Keywords:
Autoencoder Neural Network, geographic distribuition of species, enviromental features, anurans
References
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Chen, G. H., Shah, D., et al. (2018). Explaining the success of nearest neighbor methods in prediction. Foundations and Trends in Machine Learning, 10(5-6):337–588.
FrogWatch (2018). Frogwatch: Enganging citizens in science. Disponı́vel em: https: //www.naturewatch.ca/frogwatch/. Acesso em: 13 de dezembro de 2018
Fukunaga, K. (2013). Introduction to statistical pattern recognition. Elsevier.
Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow. O’Reilly Media, 1st edition
Jarvie, S. and Svenning, J. C. (2018). Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change. Philo- sophical Transactions of the Royal Society B: Biological Sciences, 373(1761).
Khan, S. S. and Madden, M. G. (2014). One-class classification: taxonomy of study and review of techniques. The Knowledge Engineering Review, 29(3):345–374.
Lorena, L. H. N., Carvalho, A. C. P. L. F., and Lorena, A. C. (2015). Filter feature selec- tion for one-class classification. Journal of Intelligent & Robotic Systems, 80(1):227– 243.
Mazhelis, O. (2006). One-class classifiers: a review and analysis of suitability in the con- text of mobile-masquerader detection. South African Computer Journal, 2006(36):29– 48.
Phillips, S. J., Anderson, R. P., and Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4):231–259
Phillips, S. J., Dudı́k, M., and Schapire, R. E. (2004). A maximum entropy approach to species distribution modeling. In Proceedings of the twenty-first international confe- rence on Machine learning, page 83. ACM.
Society, C. H. (2018). American toad. Disponı́vel em: http:// canadianherpetology.ca/species/species_page.html?cname= American%20Toad. Acesso em: 13 de março de 2019.
Theodoridis, S. and Koutroumbas, K. (2008). Pattern Recognition, Fourth Edition. Aca- demic Press, Inc., 4th edition.
WeatherUnderground (2018). Api: Weather underground. Disponı́vel em: https:// www.wunderground.com/weather/api. Acesso em: 15 de dezembro de 2018.
Published
2019-07-12
How to Cite
SILVA, Nabson; COLONNA, Juan ; CRISTO, Marco .
Prediction of anurans occupation using environmental features and Autoencoder models. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 11. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2019.6583.
