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The principal strategy of validation involves comparing model output with observable data.
The agreement of the model output with measured data was shown in Fig. 6.
The developed fuzzy model is validated by comparing the model output with experimental data.
Cattail vegetation maps (Figure 2) are used for the initial conditions as well as for comparing model output with measured data.
Therefore, any cell in the RE-ANN model output with a spatial connection to a DDE cell in D1 was treated as a DDE.
Comparison of model output with data from field experiment has played a crucial role in model development and suggested evaluation method.
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To compare the predicted model outputs with the desired output, various statistical measurements were adopted.
Therefore, by reconciling model outputs with measured data, we can achieve more accurate and reliable results.
This reconciliation of model outputs with measured data is known as calibration.
Similarly, neural network approaches can be extended to training on theoretical model outputs, with the experimental results presented for analysis.
This conclusion was supported by an uncertainty analysis on the model outputs with respect to the parameter estimation errors.
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