KDD, Data Mining, Hydrology, Evapotranspiration, Linear Regression
With the growing volume of data in various areas such as Hydrology, there is a need for using information systems to aid in handling such data. This article is a report of an experiment that used knowledge discovery techniques to estimate an important component of the hydrological cycle: evapotranspiration. The experiment reported in this article was done with weather data and showed that some algorithms, such as M5P, present good results when compared to historical data of the estimated evapotranspiration.
Xavier, Fernando; Tanaka, Asterio Kiyoshi; and Revoredo, Kate Cerqueira, "Application of Knowledge Discovery in Databases in Evapotranspiration Estimation: an Experiment in the State of Rio de Janeiro" (2015). Proceedings of the XI Brazilian Symposium on Information Systems (SBSI 2015). 76.