Alternative Models in Precipitation Analysis

Precipitation time series intrinsically contain important information concerning climate variability and change. Well-fit models of such time series can shed light upon past weather related phenomena and can help to explain future events. The objective of this study is to investigate the application of some conceptually different methods to construct models for large hydrological time series. We perform a thorough statistical analysis of the time series, which covers the identification of the change points in the time series. Then, the subseries delimited by the change points are modeled with classical Box-Jenkins methods to construct ARIMA models and with a computational intelligence technique, gene expression programming, which produces non-linear symbolic models of the series. The combination of statistical techniques with computational intelligence methods, such as gene expression programming, for modeling time series, offers increased accuracy of the models obtained. This affirmation is illustrated with examples.