Search

Objectives: On 5th September 2022, an Ms 6.8 earthquake struck the Luding County, Ganzi Prefecture, Sichuan Province, China. This earthquake triggered extensive geological hazards in the moun‐ tainous area, leading to serious casualties. Rapidly and accurately obtaining the spatial distribution of the in‐ duced geological hazards is crucial for emergency decision-making and rescue after an earthquake. Methods: Based on the global coseismic landslide database and deep learning algorithm, this paper built a near real-time prediction model of spatial distribution probability of coseismic landslides, and obtained the prediction results of the geological hazards induced by the Luding earthquake within 2 hours after the event. Through the post-earthquake unmanned aerial vehicle （UAV） and satellite remote sensing images, ma‐ chine learning and deep learning algorithms were used to realize the automated recognition of large-scale geological hazards. A total of 3 633 earthquake-induced landslides with an area of 13.78 km2 were interpreted. Finally, the model was optimized by integrating these landslide data, and the prediction results of coseismic landslides with a broader area and higher accuracy were achieved. Results: The results show that the coseis‐ mic landslide prediction model can realize a rapid capture of spatial distribution of post-earthquake geologi‐ cal hazards, filling the blank period before the acquisition of post-earthquake remote sensing images and providing support for post-disaster emergency rescue. Conclusions: Intelligent identification technologies based on UAV and satellite remote sensing images are effective means to rapidly obtain the vital informa‐ tion of large-scale geological hazards. The achievements obtained in this paper played an important role in the emergency rescue after the Luding earthquake. [ABSTRACT FROM AUTHOR]

Public participation in disaster preparedness and mitigation activities become an important part of disaster risk management. The impact of risk perception and protection motivation theory on preparedness in various types of disasters have been widely reported, and risk attitudes have similarly been studied as a factor influencing preparedness decisions. However, the adaptability of the results on the impact of risk perception on preparedness behavior was questioned in recent years. Especially in the field of geological disasters, very limited studies about the protection motivation theory and risk attitudes have been conducted in China. Therefore, this study designed a questionnaire of people's risk perceptions and the perception of protective measures based on the protection motivation theory and individual risk attitude factors. The involved field research was conducted in Ganluo County, Liangshan Prefecture, Sichuan Province in 2020. Based on the research data, the structural equation modeling method was used to analyze the effects of threat appraisal, coping appraisal and risk attitudes on people's willingness to engage in protective behaviors. The results show that individual coping appraisals have a significant effect on the willingness to engage in adaptive behavior, and the risk attitude factor added to the extended model also shows a significant effect on the willingness to prepare for disasters. The results of the study have important practical implications for the encouragement of multilevel participation in risk management in geological hazard-prone areas. [ABSTRACT FROM AUTHOR]