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These algorithms are verified using two benchmark case studies.
The prediction results were verified using two sets of typical field data.
Subsequently, the simulator was verified using two sets of experimental results.
In addition, the experimental data is verified using two analytical models.
The proposed BIM BEAM Plus assessment framework is then verified using two sampled public housing modular flat models.
The RNN_WL model can be trained and verified using two independent data sets of hourly water levels.
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The algorithms are verified using five benchmark problems that have been widely adopted in literature.
The resulting synthesis map was verified using three different data layers.
The model is simulated and verified using three vertical well data cited from the literature.
The optimal parameters and weights were verified using three additional datasets of 824 merged HR-MS/MS spectra in total.
The advantages of the proposed method were verified using four examples involving random variables with different distributions and correlation structures.
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