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Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks
journal contributionposted on 2021-05-28, 20:23 authored by O Mudele, Alejandro FreryAlejandro Frery, L Zanandrez, A Eiras, P Gamba
This article introduces a technique for using recurrent neural networks to forecast Ae. aegyptimosquito (Dengue transmission vector) counts at neighborhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situdata in two Brazilian cities, and compared with state-of-the-art multioutput random forest and k-nearest neighbor models. The approach exploits a clustering step performed before the model definition, which simplifies the task by aggregating mosquito count sequences with similar temporal patterns.