# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 5 Experimental Result Analysis  
### Example Analysis  
To calculate diurnal variation using the SVM-RF model, a main station is selected while the remaining stations are designated as training stations. Daily variation data captured between 10 November and 19 December 2018 from all stations are used as training data to feed into the SVM-RF model. By doing so, the model is able to grasp the daily variation pattern of the region through the training data. Subsequently, data from all training stations recorded between 19 and 27 December are input into the model to compute the daily variation of the main station. This methodology allows for the extraction of meaningful insights into the diurnal variation pattern of the main station, which is inferred based on the knowledge acquired from the training data. The experimental environment of this paper is  
Figure 10: Trend of diurnal variation in the geomagnetic field within a local area of Europe, (**a**–**i**) show the variation of the geomagnetic daily variation in the region at different moments of the day. configured as follows: the CPU is AMD Ryzen 7 4800H, the GPU is NVIDIA GeForce RTX 2060, and the development language is MATLAB. To assess the algorithm's applicability, the main stations chosen were BEL, HAD, ESK, FUR, and HLP, while the remaining stations were used as training stations and modeled employing SVM-RF models. Error analysis comparing the calculated values with the true values of the model was conducted to assess the accuracy of the root mean square error and the mean absolute error analysis algorithm (see Figures 11-15). For the algorithm's reliability verification, comparisons were made with the distance weighting method, the two-factor weighting method, and the latitude difference weighting method. This comprehensive analysis aims to provide insights into the performance and applicability of the SVM-RF model when compared to other established methods, thus helping to validate its efficacy in modeling geomagnetic diurnal variation. Figure 11: Analysis chart showing the calculation results of diurnal geomagnetic variation at the HLP station, (**a**) is a plot of the imputed values versus the true values of the model proposed in this paper, (**b**) a box-and-line plot of the error distribution of the different algorithms. Figure 12: Analysis chart showing the calculation results of diurnal geomagnetic variation at the HAD station, (**a**) is a plot of the imputed values versus the true values of the model proposed in this paper, (**b**) a box-and-line plot of the error distribution of the different algorithms. Essential for evaluating the accuracy and performance of algorithms in modeling and predicting geomagnetic diurnal variations at the selected stations are the metrics  
Figure 14: Analysis chart showing the calculation results of diurnal geomagnetic variation at the ESK station, (**a**) is a plot of the imputed values versus the true values of the model proposed in this paper, (**b**) a box-and-line plot of the error distribution of the different algorithms. Figure 13: Analysis chart showing the calculation results of diurnal geomagnetic variation at the FUR station, (**a**) is a plot of the imputed values versus the true values of the model proposed in this paper, (**b**) a box-and-line plot of the error distribution of the different algorithms. Figure 15: Analysis chart showing the calculation results of diurnal geomagnetic variation at the BEL station, (**a**) is a plot of the imputed values versus the true values of the model proposed in this paper, (**b**) a box-and-line plot of the error distribution of the different algorithms. presented in Tables 2-6. Specifically, RMSE (root mean square error) represents the root mean square error, MAE (mean absolute error) signifies the mean absolute error, and Correlation indicates the Pearson correlation coefficient. These metrics play a crucial role in assessing the effectiveness of the algorithms used in this study.