On August 20, 2019, at 2 a.m., a disastrous debris flow occurred in Chediguan gully in Yinxing town, China. The debris flow destroyed the drainage groove and the bridge at the exit of the gully. In addition, the debris flow temporarily blocked the Minjiang River during the flood peak, flooding the Taipingyi hydropower station 200 m upstream and leaving two plant workers missing. To further understand the activity of the debris flow after the Wenchuan earthquake, the characteristics of this debris flow event were studied. Eleven years after the Wenchuan earthquake, a disastrous debris flow still occurred in the Chediguan catchment, causing more severe losses than those of earlier debris flows. In this paper, the formation mechanism and dynamic characteristics of this debris flow event are analysed based on a drone survey, high-definition remote sensing interpretations and other means. The catastrophic debris flow event indicates that debris flows in the Wenchuan earthquake area are still active. A large amount of dredging work in the main gully could effectively reduce the debris flow risk in the gully. In addition, it is also important to repair or rebuild damaged mitigation measures and to establish a real-time monitoring and early warning system for the high-risk gully.

In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy.