  Mosquitoes are vectors of many human diseases. In particular, Aedes \ae gypti
(Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in
Latin America and it represents a global threat. Public health policies that
aim at combating this vector require dependable and timely information, which
is usually expensive to obtain with field campaigns. For this reason, several
efforts have been done to use remote sensing due to its reduced cost. The
present work includes the temporal modeling of the oviposition activity
(measured weekly on 50 ovitraps in a north Argentinean city) of Aedes \ae gypti
(Linnaeus), based on time series of data extracted from operational earth
observation satellite images. We use are NDVI, NDWI, LST night, LST day and
TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to
previous works which use linear models, we employ Machine Learning techniques
using completely accessible open source toolkits. These models have the
advantages of being non-parametric and capable of describing nonlinear
relationships between variables. Specifically, in addition to two linear
approaches, we assess a Support Vector Machine, an Artificial Neural Networks,
a K-nearest neighbors and a Decision Tree Regressor. Considerations are made on
parameter tuning and the validation and training approach. The results are
compared to linear models used in previous works with similar data sets for
generating temporal predictive models. These new tools perform better than
linear approaches, in particular Nearest Neighbor Regression (KNNR) performs
the best. These results provide better alternatives to be implemented
operatively on the Argentine geospatial Risk system that is running since 2012.
