*Natural Hazards - Impacts, Adjustments and Resilience*

training data prediction is higher than the test dataset prediction. The *r* values for the training data and testing data are found 0.9935 and 0.8833, respectively. For the REP Tree model, the training data *r* value (= 0.9405) indicates marginally better results than that for the testing data (= 0.777). It is obvious to judge that the performance of RF model in training and testing datasets is higher than that of REP Tree model. **Figure 2** presents bar graphs comparing the mean absolute error (MAE), the root mean squared error (RMSE), and the correlation coefficient (*r*)

for both models' training and test datasets. The MAE calculates the variance in the error term by term and reduces the significance of large errors; the *RMSE* value is more concentrated on large errors than on small ones. The RF model has lower *MAE* and *RMSE* values while higher *r* value, showing that in both training and testing datasets, the RF model provides adequate prediction of liquefaction-induced

*Evaluation of Liquefaction-Induced Settlement Using Random Forest and REP Tree Models:…*

*DOI: http://dx.doi.org/10.5772/intechopen.94274*

**Figure 4**.

**267**

*Training and testing of the REP tree model.*

**Figure 3.** *Training and testing of the RF model.*

*Evaluation of Liquefaction-Induced Settlement Using Random Forest and REP Tree Models:… DOI: http://dx.doi.org/10.5772/intechopen.94274*

for both models' training and test datasets. The MAE calculates the variance in the error term by term and reduces the significance of large errors; the *RMSE* value is more concentrated on large errors than on small ones. The RF model has lower *MAE* and *RMSE* values while higher *r* value, showing that in both training and testing datasets, the RF model provides adequate prediction of liquefaction-induced

**Figure 4**. *Training and testing of the REP tree model.*

training data prediction is higher than the test dataset prediction. The *r* values for the training data and testing data are found 0.9935 and 0.8833, respectively. For the REP Tree model, the training data *r* value (= 0.9405) indicates marginally better results than that for the testing data (= 0.777). It is obvious to judge that the performance of RF model in training and testing datasets is higher than that of REP Tree model. **Figure 2** presents bar graphs comparing the mean absolute error (MAE), the root mean squared error (RMSE), and the correlation coefficient (*r*)

*Natural Hazards - Impacts, Adjustments and Resilience*

**Figure 3.**

**266**

*Training and testing of the RF model.*

settlement. Additionally, the results of training and testing were shown in **Figures 3** and **4**, showing the projected settlements are plotted with the actual data. One can see that settlements were predicted more accurately by the RF model than by the REP Tree model. While the REP Tree model few settlements cases are relatively under predicted as compared to the RF model.
