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Importance Of Data Analysis

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Machine Learning Techniques
Since the plasmapause location is a continuous variable, techniques like regression support vector machine (SVM), tree bagger, and neural network were used to construct the model. In this study, the plasmapause location derived from 2013 March RBSP data is used, which is 89 observational data in total.
For neural network approaches, 70% of the electron density data were used as the training data sets, the 15% were used as the validation data and the final 15% as the testing data sets. As for the rest of the method, 75% of electron density data were randomly selected to train the data and the rest of the data were used as test datasets.
After the model were obtained, the performances of the model were accessed …show more content…

Case 1 refers to the eight parameters (Vsw, SymH, AE, AL, AU, Pressure, E, and Bz) without any manipulations. For case 2 and 4, the 12-hour moving average of Bz, symH and AE values were used instead of the real-time values. This would add time-history to these parameters. Meanwhile, in case 3 and 4, log10 of symH, AL and Bz were used instead. These parameters were selected for manipulation through a trial and error process as well as based on previous studies and understanding of the parameters.
From Figure 2, it is clear that the values predicted by bagging, boost and neural network methods came very close to actual values. On the other hand, the SVM with radial basis function (RBF) kernel and SVM with 3 order of polynomial model generated a horizontal straight line, i.e. did not change at all. Similar trends (shown in Figure 4) were observed for the rest of the parameter sets. This shows that SVM has a poor performance in general as compared to other methods.

Figure 4 illustrated the predicted values from each model using the input parameter from case 2 and the actual Lpp values.
To further examine the performance of each technique, coefficient R and RMSE from each test case were studied carefully. The results were summarized in Table 1.

Table 1 listed the RMSE and R from each model using different parameter sets.
Parameter Sets
Case 1: Original
Case 2: Original with movmean on Bz, symH, E
Case 3: Log10(SymH, AL, Bz)
Case 4: Log10(SymH, AL,

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