Use of spatial visualization for pattern discovery in evapotranspiration estimation

In Water Resources area, data are obtained from various sources, such as measuring instruments and satellites. Often such data may contain patterns that are not easily identified, either because of the large volume of data sets or because the analysis requires the use of several data dimensions. In this way, this study proposes the application of machine learning resources and spatial visualization to identify patterns in the estimation of an important component of the hydrological cycle: the evapotranspiration. This work is expected to contribute to an approach to estimate evapotranspiration, using spatial resources for pattern identification and model generation.