A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

Ufuk Beyaztas, Hanlin Shang, Zaher Yaseen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Article number126380
Pages (from-to)1-19
Number of pages19
JournalJournal of Hydrology
Publication statusPublished - Jul 2021


  • Functional autoregressive
  • Hydrometeorological variables
  • River flow prediction
  • Semi-arid environment


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