  In this research, a functional time series model was introduced to predict
future realizations of river flow time series. The proposed model was
constructed based on a functional time series's correlated lags and the
essential exogenous climate variables. Rainfall, temperature, and evaporation
variables were hypothesized to have substantial functionality in river flow
simulation. Because an actual time series model is unspecified and the input
variables' significance for the learning process is unknown in practice, it was
employed a variable selection procedure to determine only the significant
variables for the model. A nonparametric bootstrap model was also proposed to
investigate predictions' uncertainty and construct pointwise prediction
intervals for the river flow curve time series. Historical datasets at three
meteorological stations (Mosul, Baghdad, and Kut) located in the semi-arid
region, Iraq, were used for model development. The prediction performance of
the proposed model was validated against existing functional and traditional
time series models. The numerical analyses revealed that the proposed model
provides competitive or even better performance than the benchmark models.
Also, the incorporated exogenous climate variables have substantially improved
the modeling predictability performance. Overall, the proposed model indicated
a reliable methodology for modeling river flow within the semi-arid region.
