Ground fissure susceptibility mapping based on factor optimization and support vector machines

For the purpose of batter evaluating ground fissure susceptibility (GFS), this study developed a hybrid model based on factor optimization and support vector machines (SVM). Firstly, an evaluation index system of GFS was established containing 15 influence factors. Then, the data sample was normalized by certainty factors (CF) for the preparation of data analysis and machine learning. In the process of factor optimization, Schmidt orthogonalization (SO) was used to reduce collinearity in the data, and Pearson correlation coefficient (PCC) analysis was utilized to estimate its effect. Following, the principal component analysis (PCA) was applied to integrate and reduce the dimension of samples, and 9 component vectors were selected for the construction of SVM eventually. In the procedure of SVM modeling, the modeling data set was composed of 140 land fissure data and 140 non-deformation points. The cross-validation (CV) method was adopted to determine the required parameters, and the performance of the completed model was verified by statistical analysis and receiver operating characteristic (ROC) curve. The prediction accuracy of the final model reached a level of 0.852 with selected parameters. Finally, the GFS values of whole data in the study area were calculated by the trained model, and the GFS map was produced with its effect assessed. In conclusion, the model in this research can evaluate the GFS accurately and has a high value in both scientific research and engineering application.