Proceedings Article | 10 July 2024
KEYWORDS: Hazard analysis, Education and training, Performance modeling, Machine learning
Geologic hazards are frequent in the southwestern region of China, posing a serious threat to the safe operation of the transmission and substation system. Therefore, it is crucial to evaluate the susceptibility of geologic hazards along power transmission lines for effective hazard prevention and mitigation within the transmission system. This study focused on five districts and counties along the high-voltage transmission lines in Chongqing City, China. Thirteen evaluation factors were considered, encompassing elevation, slope, slope aspect, plane curvature, profile curvature, lithology, distance to fault, land cover, geomorphology, rainfall, NDVI, distance to waterway, and distance to road. These factors served as the basis for weight of evidence (WOE), random forest (RF), and support vector machine (SVM) evaluation indexes for geologic hazards susceptibility evaluation and visualization analysis. The results showed that (1) the AUC values of the area under the Receiver Operating Characteristic curve (ROC) for the WOE, RF, and SVM models were 0.870, 0.883, and 0.880, respectively, with RF having the highest accuracy. (2) The proportions of high and very high susceptibility zones obtained by the three models of WOE, RF, and SVM were 58.43%, 41.92%, and 50.50%, respectively. These proportions closely aligned with the distribution of geologic hazards. The evaluation outcomes provide a more precise reflection of the potential pattern of future geologic hazards incidence in the study area.