Model evaluation criterion for quantifying accuracy in scalability studies.

The paper aims to discuss the scalability study carried out in birds and flapping wing micro air vehicle. The discussed parameter is Flapping wing frequency as dependent variable.The independent variable during research are mass, area and wing span. To find the relationship among dependent and independent variable, a training data set of 48 birds and micro air vehicle were collected from different researches. The relationships were formulated using multiple linear regression and principle component techniques and were then compared with different past proposed relationships for estimating flapping wing frequency. The software used to develop models was Minitab V17. The evaluation methodology used in this paper is based on model evaluation techniques that is using different criterion such as Mean absolute error (MAE), Root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Index of agreement, Ratio of the root mean square error to the standard deviation of measured data (RSR), Percent bias (PBIAS) and Relative efficiency of NSE & Index of agreement. In addition to model evaluation methods, model validation is also conducted using a test data set to determine the accuracy of developed models. The proposed model in this paper has performed significantly better than previous models and in both tests that is model evaluation and validation. The proposed model is more accurate is fitting the training as well as test data sets. The selection of model after application of model evaluation criteria's, was done using Pugh matrix methodology. The proposed model can be taken as the foundation for future research in this stream and utilized in improving the design efficiency of engineering products. [ABSTRACT FROM AUTHOR]

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