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Eight evaluation criteria were applied to evaluate the forecasting accuracy of seasonal precipitation.
To evaluate the forecasting accuracy of the proposed model, two commonly used criteria are employed to measure the errors: the normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) [26].
Since each method has its own advantages and limitations, it is assumed that these methods can combine to evaluate the forecasting accuracy of the models considered, thus avoiding the uncertainty and unreliability due to limitations of the individual method.
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We evaluate the forecast accuracy of the SWIFT TSUNAMI system.
We also evaluate the forecast accuracy in terms of the tide gauge observations.
In order to evaluate the forecast accuracy in "real time," we predict two steps ahead (May and June 2016).
We evaluated the forecast accuracy of the SWIFT TSUNAMI system for the eight tsunamis (M w 7.5 8.6).
There are several ways to evaluate the forecast performances.
The measure of outlierness evaluated by the first layer is used in the second layer to assess and improve the forecasting accuracy.
The persistence (PER) model [12], the common SVM model and the linear quantile regression model are selected as the competitive models to evaluate the expectation forecast accuracy of the proposed approach.
In this study, the forecasting accuracy was evaluated by eight methods (Table 2).
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