Abstract
Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of hydro-meteorological stations on northern areas, this study investigates the benefits and downfalls of univariate streamflow modeling at high latitudes using GP and seasonal autoregressive integrated moving average (SARIMA). Furthermore, a new evolutionary time series model, called GP-SARIMA, is introduced to enhance streamflow forecasting accuracy at long-term horizons in a lake-river system. The paper includes testing the new model for one-step-ahead forecasts of daily mean, weekly mean, and monthly mean streamflow in the headwaters of the Oulujoki River, Finland. The results showed that a combination of correlogram and average mutual information (AMI) analysis might yield in the selection of the optimum lags that are needed to be used as the predictors of streamflow models. With Nash-Sutcliffe efficiency values of more than 99%, both GP and SARIMA models exhibited good performance for daily streamflow prediction. However, they were not able to precisely model the intramonthly snow water equivalent in the long-term forecast. The proposed ensemble model, which integrates the best GP and SARIMA models with the most efficient predictor, may eliminate one-fourth of root mean squared errors of standalone models. The GP-SARIMA also showed up to three times improvement in the accuracy of the standalone models based on the Nash-Sutcliff efficiency measure.
Highlights
Predicting floods and streamflow, in general, is one of the most critical tasks of hydrological modeling
In comparison to the goodness-of-fit values given in Table 5, Genetic programming (GP)-seasonal autoregressive integrated moving average (SARIMA) is superior to the standalone GP and SARIMA models
Cross-correlation analysis between the new predictors and the target streamflow series showed that they have a higher correlation (0.67 and 0.63 for GP and SARIMA, respectively) than standalone models’ inputs
Summary
Predicting floods and streamflow, in general, is one of the most critical tasks of hydrological modeling. This is quite a difficult modeling task, owing to the highly nonlinear, time- and spatially varying nature of the underlying process (Cheng et al 2020). It is time-consuming and costly to measure the processes that affect streamflow, in tributaries and snow-fed rivers, which means that the use of remotely sensed data is inevitable for accurate forecasts (Yang et al 2007). It is more satisfactory to use univariate artificial intelligence (AI) techniques in which the preceding streamflow records are merely used to construct a predictive model (Zhang et al 2018)
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