## 6 Conclusions  
In this study, the aim was to address the problems of limited data sources for traditional landslide monitoring methods and the lack of effective methods to excavate the  
Figure 8: Distribution of landslides by determined susceptibility classes. spatial distribution characteristics of landslide disasters and their triggering factors. We presented a landslide susceptibility evaluation method by considering InSAR deformation. The presented method uses historical landslide data, high-resolution satellite remote sensing, unmanned aircraft remote sensing, and surface deformation information of the study area by inverting the InSAR technology, combining engineering geology principles and landslide interpretation signs for the early identification of regional landslides. Moreover, we established 122 landslide databases after field verification. After screening, we selected 13 evaluation factors (i.e., elevation, slope, slope direction, curvature, average annual rainfall, vegetation normalization index, stratigraphic lithology, distance from faults, line of equations, distance from rivers, distance from roads, and rate of descending and ascending deformation), and we developed an evaluation index system by using the above-mentioned factors to build a dataset of landslides in the study area. Next, we evaluated the susceptibility of landslides in the study area by using the BO-CNN model from the perspective of disaster prevention and mitigation. We tested the screened evaluation factors for independence. The 13 evaluation factors were independent of each other, which can ensure the accuracy of the susceptibility model. We analyzed the relationship between landslides and indicator factors based on the frequency ratio method by grading all evaluation factors. The results indicated that the categories of the impact factors identified by using the frequency-ratio model included the elevation of low mountains, slope gradient of sharp, steep and dangerous slopes, slope direction of south-east and south-west slopes, topographic curvature less than or equal to 0, average annual rainfall of less than 1300 mm, 1400-1500 mm and more than 1600 mm, NDVI 0-0.6, distance from roads 0-400 m, distance from rivers 0-400 m and 600-800 m, the stratigraphic lithology of P, C, Ey\(\delta\)\(\pi\), the distance from the fault of 0-300 m and more than 1200 m, the isoseismic line of VII and VII outside (10 km), the rate of deformation of the descending rail of more than \(-5\) mm/y, and the rate of deformation of the ascending rail of less than \(-50\) mm/y and more than 5 mm/y, which can indirectly or directly contribute to the occurrence of landslides. We implemented PSO-SVM, BO-RF, and BO-CNN landslide susceptibility models by using the Python programming language, and we carried out susceptibility evaluation. To determine the accuracy of the models, we selected ROC and AUC as the model accuracy indicators in this study. The calculation results indicated that the ROC curves of the three models were close to the upper left corner, and the AUC values of the three models reached 0.9388, 0.9529, and 0.9535, respectively. Specifically, the BO-CNN model obtained the most effective ROC curve and AUC value. We analyzed the distribution of susceptible zones in the study area, and we adopted the three models and the FR to validate the feasibility of the models with the percentage of the number of landslides in the susceptible zones predicted. The experimental results revealed that the proportion of landslides in the high susceptibility zone of PSO-SVM, BO-RF, and BO-CNN reached 76.10%, 84.39%, and 81.37%, respectively, which indicated that the landslides in the study area were primarily distributed in the high susceptibility zone in line with the actual situation. Furthermore, the FR value of landslides in the high susceptibility zone of BO-CNN reached up to 14.9, and the FR value was pronouncedly higher than 1, which can validate a stronger correlation.