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

BEYAZTAŞ U., Shang H. L., Yaseen Z. M.

Journal of Hydrology, cilt.598, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 598
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.jhydrol.2021.126380
  • Dergi Adı: Journal of Hydrology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Communication Abstracts, Environment Index, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: River flow prediction, Hydrometeorological variables, Functional autoregressive, Semi-arid environment, NEURAL-NETWORKS, WEST-AFRICA, HYDROLOGY, INTERVALS, PATTERNS, PACKAGE, DEMAND, BASIN
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2021 Elsevier B.V.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.