# Data-Driven Pavement Performance: Machine Learning-Based Predictive Models
## 6 Results  
Prediction of output variables using all the models along with MSE and R\({}^{2}\) values is presented. Each model uses a specified number of folds based on the extent of output variables. For number of passes to fatigue and rut damage, the MSE increases for all the models; however, when rut depth and fatigue life reduction are evaluated, the MSE stays the minimum. Furthermore, the data scatter for predicted values and actual values is shown along the perfect prediction line. Figure 7 shows the performance prediction in terms of fatigue life reduction for different models. Figure 7a shows comparison of actual calculated and predicted values using the decision tree algorithm. Fatigue life reduction in terms of reduced number of years as a result of zero wander mode are predicted based on the input data. As observed, a total  
Figure 7: Fatigue life reduction prediction of different models: (**a**) decision tree; (**b**) gradient boosting; (**c**) KNN; (**d**) linear regression; (**e**) random forest; (**f**) SVR; (**g**) CatBoost; (**h**) LightGBM. of nine optimized folds are developed using the k-fold technique, and the maximum proficiency of the model was contained against the nine folds. All the folds generate an MSE of 0.00. However, Fold 9 kept the predicted values closer to the actual values. Figure 7b shows the comparison of values using the gradient boost. As observed, the predicted values lie closer to the centre line, and all folds exhibit the same performance. A total of nine folds have been developed for further optimization of this algorithm. Figure 7c shows the comparison of predicted reduced fatigue life in number of years and actual fatigue life reduction as used in input using the KNN algorithm. As observed, the predicted values for all the folds are scattered with MSE of 0.04 among all the folds. KNN shows lower performance prediction efficiency as compared to other analysed models. In Figure 7d, linear regression has been used to evaluate the prediction performance for reduced life in number of years as a result of fatigue damage. A certain pattern can be observed for all the folds analysed using linear regression. Based on the prediction performance evaluation, linear regression yields the minimum MSE of 0.05 for Fold 9 and Fold 3. However, the overall predictive performance of linear regression model is less than that of the K-neighbour algorithm. In Figure 7e, the random forest algorithm has been used to evaluate the correlation of predicted values with actual values. This algorithm yields the MSE of 0.00 for the majority of folds. However, the scatter in prediction is higher than that of the gradient boosting algorithm. In Figure 7f, SVR has been used to predict the reduction in fatigue life in years. SVR only requires a total of five folds to perform optimization for a model. Furthermore, SVR yields the MSE of 0.00 for all the folds considered. However, the data scatter is higher than that of the gradient boosting model. Prediction of reduced fatigue life and comparison with actual values are shown in Figure 7g. As observed, the minimum MSE is obtained at 0.000 and R\({}^{2}\) of 1.0000. CatBoost regression outperforms other models in terms of prediction accuracy and matching with the actual values for fatigue life reduction in number of years. Figure 7h above shows the comparison of predicted and actual values using LightGBM. As observed, the model yields accurate results with MSE of 0.001 and R\({}^{2}\) of 0.999. LightGBM is the second-best machine learning model after CatBoost. MSE and R\({}^{2}\) values for prediction of fatigue life reduction in years are shown in Table 1. As observed, the highest MSE is shown by linear regression with a value of 0.056 and a corresponding lowest R\({}^{2}\) value of 0.9023. Furthermore, CatBoost regression exhibits the highest prediction performance among other models with an MSE of 0.000 and R\({}^{2}\) of 1.000. Gradient boost and LightGBM exhibit the least scatter in predicted data values showing close correspondence to the actual values.