Rocscience Slide 6 Extra Quality Keygen 37

Rocscience Slide 6 Extra Quality Keygen 37

Rocscience Slide 6 Keygen 37 --->>> https://shoxet.com/2ta9Ls

Thus, in this study, we aimed to create an integrated method to optimize the LSM and SBAS results that can be used in the landslide susceptibility risk assessment for the Hong Kong mountainous areas. In particular, we aimed to reduce the misclassification of the LSM. The method included two parts: a new LSM optimized from the SBAS results and a procedure for removing the effect of terrain deformation affected by slope movement from the LSM. The study area is shown in Figure 2, and the locations of all landslide data used in this study are shown in Figure 1.The results of the above two parts are shown in Figure 3. The corresponding validation and testing results of the new LSM are shown in Figure 4. The confusion matrix results of the new LSM combined with the SBAS results are shown in Figure 5. In this study, the landslide susceptibility degree is classified into five categories: very low (0–1), low (2–3), moderate (4–6), high (7–9), and very high (10–13). The landslide activity was then mapped and classified into four categories: low (1–3), moderate (4–6), high (7–9), and very high (10–13). The results of the SBAS are shown in Figure 7. The results of the combined SBAS and new LSM are shown in Figure 8. In addition, the LSM is integrated with a new SBAS-InSAR-based deformation monitoring system using the Random Forest model in which the landslide susceptibility degree and the landslide activity were classified into five categories: very low (0–1), low (2–3), moderate (4–6), high (7–9), and very high (10–13). The results of the new SBAS-InSAR-based monitoring system are shown in Figure 10. Finally, the results of the new LSM combined with the SBAS-InSAR-based deformation monitoring system are shown in Figure 11. The LSM was optimized using the new SBAS results, and the landslides were classified into six categories: very low (0–1), low (2–3), moderate (4–6), high (7–9), very high (10–11), and low-slope stability (12). The validation results of the new LSM combined with the SBAS-InSAR-based deformation monitoring system are shown in Figure 15.

In this study, the results of the 1st type of landform are used as the reference standard for comparison, and the other two types of landform were used to predict the landslides. A total of 194 landslides were selected as the test set. The results of the validation and the AUC values of the ROC curve of the random forest and logistic regression are shown in Table 5. The validation results of the slope degree of the random forest model were relatively low, which results in the LSM being over-prediction, while the results of the logistic regression model were relatively high. Therefore, the random forest model was selected to participate in the landslide LSM optimization. The contingency matrix based on the susceptibility degree, the Vslope, and the three types of landforms are shown in (Table 6). The new LSM significantly improved the landslide occurrence probability distribution in the study area. The newly generated LSM maps can be used for the preliminary assessment of landslide susceptibility at a regional scale. 827ec27edc