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The performance of the neural network model was evaluated from the indices of root mean square error, coefficient of efficiency, error of peak discharge, and error of time to peak.
Six statistical indices (mean absolute error, root mean square error, correlation coefficient, error of time to peak discharge, error of peak discharge, and coefficient of efficiency) are employed to assess prediction performance.
The models performance were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge and error of time for peak to arrive.
Suppose that δt is the error of time quantization.
Results show that prediction error of time to failure is less than 10%%.
We compared the filtering effect of PCA and ICA by analyzing the reductions of root mean square (RMS) error of time series in these two regions.
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We suggest an algorithm that quantifies the discretization error of time-dependent physical quantities of interest (goals) for numerical models of geophysical fluid dynamics.
We estimate the discretization error of time-dependent goals that are calculated from a numerical model of the spherical shallow-water equations.
In this work, we propose a method to bound the error of time-series aggregation by measuring the accuracy of the aggregation in the domain of the objective function: For each design, the error is calculated between the cost considering the aggregated time series and the full time series.
The wind power forecasting errors at a specific time are related to the error of previous time.
Because perforation shot origin times are inaccurate, we use the RMS error of the time double-difference (DDrms) value, which can be computed from first arrival time differences.
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