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In general, the seasonal model better forecasted precipitation in spring, autumn, and winter than the global model; but the global model did better in forecasting precipitation in summer.
The seasonal model better forecasted precipitation changes in spring, autumn, and winter than did the global model, but did worse than the global model in forecasting precipitation changes in summer.
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However, MAPE indicated differently, that is, the seasonal and global models better forecasted precipitation changes in spring and summer than in autumn and winter.
Visual examination of Fig. 10 shows that the ARMA model better forecasts values after 2008.
(3) In both the track precipitation forecasting and cross-validation precipitation forecasting practices, the seasonal model better forecasts precipitation changes in spring, autumn, and winter; however, the global model better forecasts summer precipitation changes.
Comparing with the model neglecting the proton concentration change and dynamic diffusion of vanadium ions, the presented model can better forecast the performances of the VFB.
Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model.
However, if a flow is longer-lived and cooling-limited (e.g., Etna 1983 or Mauna Loa 1984; Guest et al., 1987; Lipman and Banks, 1987), fully thermorheological models (OpenFOAM and FLOW-3D) may better forecast its advance.
The experimental results show that the proposed model produces better forecasting results than several existing models.
In another aspect, Azizi [1] shows that ANFIS model provides better forecasting accuracy in comparison with Bayesian model.
The seasonal and global models were then compared to determine which model produces better forecasts.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com