Research Article
Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments
Table 5
Performance statistics of the ANN model during evaluation: mean absolute error (MAE) for the data classes.
| Target data versus output data cm | S15 | S30 | S60 | Maximum absolute error cm | 98.24 | 111.34 | 117.37 | Class | Percentage % | Water level class limits cm | MAE |
| 1 | 100, 75 | 210.5, 174.65 | 82.54 | 100.47 | 101.32 | 2 | 75, 50 | 174.65, 138.76 | 38.7 | 53.30 | 59.80 | 3 | 50, 25 | 138.76, 102.88 | 13.55 | 24.94 | 34.39 | 4 | 25, 0 | 102.88, 66.99 | 0.56 | 0.74 | 0.84 | 5 | 0, −25 | 66.99, 65.61 | 0.29 | 0.44 | 0.71 | 6 | 25, −50 | 65.61, 64.23 | 0.23 | 0.67 | 1.35 | 7 | 50, −75 | 64.23, 62.85 | 0.23 | 0.41 | 0.44 | 8 | 75, −100 | 62.85, 61.47 | 0.21 | 0.28 | 0.45 |
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