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In this research, a functional time series model was introduced to predictfuture realizations of river flow time series. The proposed model wasconstructed based on a functional time series's correlated lags and theessential exogenous climate variables. Rainfall, temperature, and evaporationvariables were hypothesized to have substantial functionality in river flowsimulation. Because an actual time series model is unspecified and the inputvariables' significance for the learning process is unknown in practice, it wasemployed a variable selection procedure to determine only the significantvariables for the model. A nonparametric bootstrap model was also proposed toinvestigate predictions' uncertainty and construct pointwise predictionintervals for the river flow curve time series. Historical datasets at threemeteorological stations (Mosul, Baghdad, and Kut) located in the semi-aridregion, Iraq, were used for model development. The prediction performance ofthe proposed model was validated against existing functional and traditionaltime series models. The numerical analyses revealed that the proposed modelprovides competitive or even better performance than the benchmark models.Also, the incorporated exogenous climate variables have substantially improvedthe modeling predictability performance. Overall, the proposed model indicateda reliable methodology for modeling river flow within the semi-arid region.

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