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Erratum to: Water Resour Manage
DOI 10.1007/s11269-012-0089-y
The original version of this article unfortunately contained a mistake in ANNEX A: the Nash-Sutcliffe indexes were reported incorrectly. The correct table is given in the succeeding pages. In addition, a Figure concerning the same ANNEX A is now provided.
ANNEX A
Table 4 Model performances in multi ahead forecasting (from 2 to 6 months ahead)
Model | Forward forecasting time-lag | Indices of performances and efficiency | ||||
---|---|---|---|---|---|---|
Correlation coefficient | Root mean squared error | Fractional standard error | Nash Sutcliffe index | Bias | ||
[Months] | (R) | (RMSE) | (FSE) | (E) | (B) | |
MLR | 2 | 0.802 | 0.056 | 0.031 | 0.622 | 0.994 |
3 | 0.800 | 0.055 | 0.031 | 0.621 | 0.995 | |
4 | 0.799 | 0.055 | 0.031 | 0.627 | 0.998 | |
5 | 0.803 | 0.054 | 0.030 | 0.638 | 0.999 | |
6 | 0.832 | 0.050 | 0.028 | 0.689 | 0.999 | |
ANNRaw | 2 | 0.813 | 0.051 | 0.029 | 0.655 | 0.997 |
3 | 0.799 | 0.051 | 0.029 | 0.633 | 0.999 | |
4 | 0.789 | 0.052 | 0.029 | 0.619 | 1.000 | |
5 | 0.171 | 0.096 | 0.054 | -0.270 | 1.001 | |
6 | 0.796 | 0.052 | 0.029 | 0.6310 | 1.002 | |
WANNHaar | 2 | 0.879 | 0.042 | 0.024 | 0.766 | 1.000 |
3 | 0.835 | 0.047 | 0.026 | 0.696 | 0.998 | |
4 | 0.851 | 0.044 | 0.025 | 0.722 | 1.001 | |
5 | 0.303 | 0.092 | 0.052 | -0.166 | 1.001 | |
6 | 0.768 | 0.055 | 0.031 | 0.587 | 1.000 | |
WANNdb2 | 2 | 0.889 | 0.040 | 0.023 | 0.788 | 1.000 |
3 | 0.846 | 0.045 | 0.025 | 0.712 | 1.001 | |
4 | 0.849 | 0.045 | 0.025 | 0.719 | 0.999 | |
5 | 0.326 | 0.090 | 0.051 | -0.118 | 1.000 | |
6 | 0.792 | 0.052 | 0.029 | 0.627 | 1.000 | |
WANNdb3 | 2 | 0.875 | 0.043 | 0.024 | 0.758 | 0.999 |
3 | 0.847 | 0.045 | 0.025 | 0.713 | 1.000 | |
4 | 0.843 | 0.046 | 0.026 | 0.707 | 0.999 | |
5 | 0.254 | 0.094 | 0.053 | -0.214 | 0.999 | |
6 | 0.769 | 0.055 | 0.031 | 0.590 | 0.999 | |
WANNdb4 | 2 | 0.788 | 0.054 | 0.030 | 0.613 | 0.998 |
3 | 0.772 | 0.054 | 0.030 | 0.591 | 1.000 | |
4 | 0.750 | 0.056 | 0.031 | 0.557 | 1.000 | |
5 | 0.445 | 0.079 | 0.044 | 0.148 | 1.001 | |
6 | 0.716 | 0.060 | 0.034 | 0.505 | 1.004 | |
WANNdb5 | 2 | 0.794 | 0.054 | 0.030 | 0.619 | 0.997 |
3 | 0.731 | 0.058 | 0.032 | 0.529 | 0.998 | |
4 | 0.687 | 0.062 | 0.034 | 0.466 | 0.999 | |
5 | 0.410 | 0.080 | 0.045 | 0.129 | 1.000 | |
6 | 0.732 | 0.058 | 0.033 | 0.534 | 0.999 |
![figure a](http://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs11269-012-0122-1/MediaObjects/11269_2012_122_Figa_HTML.gif)
Fig. 11 Annex figure Performances of multi months ahead forecasting: Correlation coefficient (R), Root Mean Squared Error (RMSE), Fractional Standard Error (FSE), and Nash Sutcliffe index (E) of seven models: Multi Linear Regression, ANN’s raw data forecasting, and ANN coupled with wavelets of type: Haar, db2, db3, db4, db5
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The online version of the original article can be found at http://dx.doi.org/10.1007/s11269-012-0089-y
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Campisi-Pinto, S., Adamowski, J. & Oron, G. Erratum to: Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy. Water Resour Manage 27, 319–321 (2013). https://doi.org/10.1007/s11269-012-0122-1
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DOI: https://doi.org/10.1007/s11269-012-0122-1