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Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

journal contribution
posted on 28.05.2021, 20:23 by O Mudele, Alejandro 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.

History

Preferred citation

Mudele, O., Frery, A., Zanandrez, L., Eiras, A. & Gamba, P. (2021). Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4390-4404. https://doi.org/10.1109/JSTARS.2021.3073351

Journal title

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

14

Publication date

01/01/2021

Pagination

4390-4404

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

ISSN

1939-1404

eISSN

2151-1535