# Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
## 4 Prediction Results
### Training Results of the Five Machine Learning Models  
The purpose of the comparison experiments in this section is to identify the most suitable model to replace numerical calculations among the five machine learning models. The accuracy of each trained model is evaluated by r2_score, calculated based on validation and test dataset. When the predicted results are too far away from the r2_scores, the r2_score values show negative abnormally.  
Figure 10 shows the comparison of different models under the self condition. Datasets 1 and 2 are sampled on the global scale, while datasets 3 and 4 add local data based on global data. Because the amount of data in dataset 1 is small, and the sampling points in the global scope are relatively sparse, the r2_score of the test dataset is all negative. Adding global data to dataset 1, the r2_score of the test datasets of RF, ET, and DT become positive (0.7706, 0.3787, and 0.6148). Although the r2_score of KN and GBDT are improved by the addition of global data, the value is still negative. After dataset 3 and dataset 4 add training data near the test points, the accuracy of all the assessed models is greatly  
Figure 8: Normalization experiment of the self condition.  
improved. However, the histogram of data 4 indicates that the KN and GBDT models still underperform the other three models based on the r2_score.  
As a result, the KN and GBDT models are first excluded, and the appropriate model is selected from the remaining three. DT shows the best performance with dataset 1, but the accuracy becomes lower than RF and ET as the dataset is expanded. Although the accuracy of ET is lower than RF and DT after the increase in local data, the performance of the validation dataset is always worse than that of RF and DT, and the performance on global data is not as good as RF and DT, indicating that ET has a higher accuracy rate depending on data density. Compared with ET, RF has the highest r2_score of the test dataset on the global data. With the addition of local data, the r2_score of the test dataset reaches 0.9999. Therefore, RF is found to be the most suitable machine learning model for the self condition.  
Figure 11 shows the comparison of different models under the operation condition. Dataset 1 is the data sampled on the global scale, dataset 2 includes local data around the test points based on global data, and dataset 3 further adds local data based on dataset 2. There are few data sampled in the global scope of the operation, and the new local data in dataset 2 are also scarce. Because the operation condition has a large number of input parameters and involves complex calculations, the performance of the models trained by datasets 1 and 2 is not satisfactory. The accuracy of the model trained by dataset 3 is significantly improved due to the addition of local data. Given the poor accuracy found with the first three datasets, the KN, DT, and GBDT models are first excluded. Although the ET model outperforms the RF model with dataset 2, the relationship is not conclusive, given the small dataset size. Since the validation dataset result of the RF model is consistently better than the ET model, the RF model is regarded as the best among the five assessed machine learning models.  
Figure 9: Normalization experiment of the operation condition.  
Figure 11: Comparison of different models under the operation condition.  
Figure 10: Comparison of different models under the self condition.