Risk Analysis最新文献

The prediction of unmanned aerial vehicle (UAV) operators' unsafe acts is critical for preventing UAV incidents. However, there is a lack of research specifically focusing on UAV operators' unsafe acts, and existing approaches in related areas often lack precision and effectiveness. To address this, we propose a hybrid approach that combines the Human Factors Analysis and Classification System (HFACS) with random forest (RF) to predict and warn against UAV operators' unsafe acts. Initially, we introduce an improved HFACS framework to identify risk factors influencing the unsafe acts. Subsequently, we utilize the adaptive synthetic sampling algorithm (ADASYN) to rectify the imbalance in the dataset. The RF model is then used to construct a risk prediction and early warning model, as well as to identify critical risk factors associated with the unsafe acts. The results obtained through the improved HFACS framework reveal 33 risk factors, encompassing environmental influences, industry influences, unsafe supervision, and operators' states, contributing to the unsafe acts. The RF model demonstrates a significant improvement in prediction performance after applying ADASYN. The critical risk factors associated with the unsafe acts are identified as weak safety awareness, allowing unauthorized flight activities, lack of legal awareness, lack of supervision system, and obstacles. The findings of this study can assist policymakers in formulating effective measures to mitigate incidents resulting from UAV operators' unsafe acts.

There is growing interest in leveraging advanced analytics, including artificial intelligence (AI) and machine learning (ML), for disaster risk analysis (RA) applications. These emerging methods offer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on "learning" through datasets. There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. These existing methods are generally accepted by the risk community and are grounded in use across various risk application areas. The next frontier in RA with emerging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consideration of usefulness, trust, explainability, and other factors. This article leverages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI-based method for disaster RA applications. This article utilizes empirical evidence from expert perspectives to support key insights on those methods and the compatibility of those methods. This article will be of interest to researchers and practitioners in risk-analytics disciplines who leverage AI/ML methods.

Extreme heat events are more frequent and intense as a result of global climate change, thus posing tremendous threats to public health. However, extant literature exploring the multidimensional features of heat-health risks from a spatial perspective is limited. This study revisits extreme heat-health risk and decomposes this concept by integrating multi-sourced datasets, identifying compositional features, examining spatial patterns, and comparing classified characteristics based on local conditions. Using Maryland as the focal point, we found that the components of heat-health risk are different from traditional risk dimensions (i.e., vulnerability, hazards, and exposure). Through a local-level clustering analysis, heat-health risks were compared with areas having similar features, and among those with different features. The findings suggest a new perspective for understanding the socio-environmental and socio-spatial features of heat-health risks. They also offer an apt example of applying cross-disciplinary methods and tools for investigating an ever-changing phenomenon. Moreover, the spatial classification mechanism provides insights about the underlying causes of heat-health risk disparities and offers reference points for decision-makers regarding identification of vulnerable areas, resource allocation, and causal inferences when planning for and managing extreme heat disasters.

Online knowledge-sharing platforms construct risk knowledge and provide the audience with risk-related scientific facts. We study how speakers organize narratives in past, present, and future foci to influence the audience's emotions through the audience's appraisal of motive congruency and coping potential. Empirical evidence from 210 Technology, Entertainment, Design talks about disasters from 2002 to 2018 demonstrates that emphasizing the past, present, and future in risk narrative leads to the audience's comments with more negative, less positive, and more positive emotions, respectively. Concrete (vs. abstract) portrayal of the risk narrative improves the audience's situational awareness, enhances their risk appraisal, and intensifies the impact of temporal focus on emotions, providing evidence of how temporal focus impacts. These findings demonstrate that temporal focus can effectively reduce risk overreaction or ignorance and facilitate emotion regulation in risk communication.

The vulnerability of mega infrastructure projects (MIPs) has generated online public opinion crises, leading to public trust damage. However, few studies focused on the online dynamic trust of MIPs in such crises from the perspective of multiple users. Based on situational crisis communication theory, this study aims to explore the dynamic public trust in MIPs during online public opinion crises of extreme climate emergencies. The extreme heavy rainstorm event in Zhengzhou City, China, was selected as the case. Content analysis, the curve fitting method, and sentiment analysis were conducted to process the collected data from multiple users. The results indicated that the opinions of trust damage were set by "media practitioners" and led by "elites," whereas the opinions of trust repair were directed by "elites," led by "media practitioners," and defended by "individuals." Besides, trust dimensions would change over time; integrity-based and competence-based trust diffused alternatively. "Diminish," "deny," and "rebuild" strategies were proved to be the most effective strategies in integrity-based, competence-based, and competence and integrity-based trust repair, respectively. The findings can contribute to the authorities monitoring online public opinions in extreme climate emergencies and repairing trustworthy images.