  In river flow analysis and forecasting there are some key elements to
consider in order to obtain reliable results. For example, seasonality is often
accounted for in statistical models because climatic oscillations occurring
every year have an obvious impact on river flow. Further sources of alteration
could be caused by changes in reservoir management, instrumentation or even
unexpected shifts in climatic conditions. When these changes are ignored the
statistical results can be strongly misleading. This paper develops an
automatic procedure to estimate number and locations of changepoints in
Periodic AutoRegressive models. These latter have been extensively used for
modelling seasonality in hydrology, climatology, economics and electrical
engineering, but there are very few papers devoted also to changepoints
detection, moreover being limited to changes in mean or variance. In our
proposal we allow the model structure as a whole to change, and estimation is
performed by optimizing an objective function derived from the Information
Criterion using Genetic Algorithms. The proposed methodology is brought out
through the example of three river flows, for which we built models with
possible changepoints and evaluated their forecasting accuracy by means of Root
Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. The
last years of data sets have been omitted from the selection and estimation
procedure and were then used to forecast. Comparisons with literature on river
flow forecasting confirms the efficiency of our proposal.
