Abstract
Deep Learning has achieved great successes in various complex tasks such as image classification, detection and natural language processing. This work describes the process of designing and implementing seven deep learning approaches to perform regressions on mosquito populations from a specific region, given co-variables such as humidity, uv-index and precipitation intensity. The implemented approaches were: Recurrent Neural Networks (LSTM), an hybrid deep learning model, and a Variational Autoencoder (VAE) combined with a Multi-Layer Perceptron (MLP) which instead of using normal RGB images, uses satellite images of twelve channels from Copernicus Sentinel-2 mission. The experiments were executed on the Washington Mosquito Dataset, augmented with weather information. For this dataset, an MLP proved to achieve the best results.
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Suarez-Ramirez, C.D., Duran-Vega, M.A., Sanchez C., H.M., Gonzalez-Mendoza, M., Chang, L., Marshall, J.M. (2021). Deep Learning Architectures Applied to Mosquito Count Regressions in US Datasets. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Computational Intelligence. MICAI 2021. Lecture Notes in Computer Science(), vol 13067. Springer, Cham. https://doi.org/10.1007/978-3-030-89817-5_15
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