The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction

In this study, post-liquefaction Standard penetration test (SPT) data from the Chi-Chi earthquake was collected and included into a Random Forest (RF) and Support Vector Machine (SVM) model by using a metaheuristic-based optimizer called the Cat Swarm Optimization (CSO). This was done in an effort to improve the accuracy of the projections across a number of different iterations. After that, the data were normalized, and a person correlation matrix and a chi-square test were used to establish the degree of link between the variables. After that, we chose the parameters for the RF and SVM models that would be used for both training and testing by using a random sampling technique. This allowed us to have complete control over the models. It was discovered that CSO not only improved the fitting of the model and the quality of the results, but it also speed up the procedure.