Toward a framework for systemic multi-hazard and multi-risk assessment and management

Dynamics of natural hazard risk awareness: Panel analysis insights from Switzerland

The analysis of risk awareness should be the initial stage in integrated natural hazard risk management to promote appropriate and effective measures for mitigating risks and strengthening social resilience inside the multi-risk framework. Nevertheless, earlier studies focused on cross-sectional data and overlooked the changes in risk awareness levels and associated independent variables with time. This study analyzes for the first time a balanced nationwide panel dataset of 1612 respondent-year observations from Switzerland (period 2015-2021, including the epidemic of COVID-19) to examine and compare the effects of potential independent variables on the four dimensions of natural hazard risk awareness (NHRA), ranging from the broadest dimension of Relevance to higher dimensions of Perceived Probability of an event, Perceived Threat to life and valuables, and Perceived Situational Threat. The analysis in this study incorporates multiple methods of Random-Effect Model (RE), Generalized Linear Model (GLM), and mediation analysis. Results show that NHRA increased in Switzerland to different extents (up to 23.24%) depending on the dimension. Event memory, perceived information impact and reported individual informed level appeared to be the most consistent independent variables positively influencing panel NHRA. Among these, perceived information impact as an important indicator of risk communication, was also found to serve as a mediator from risk preparedness to risk awareness. By encouraging residents to engage in "Begin Doing Before Thinking" (BDBT) programs to leverage subliminal effects and self-reflection, this study proposes that behavior-cognition feedback loops may facilitate a virtuous cycle. Our promising observations provide recommendations for an effective awareness-rising strategy design and suggest extensive insights from potential short-interval panel analysis in the future.

A single-building damage detection model based on multi-feature fusion: A case study in Yangbi

Accurate and effective identification, determination of the location, and classification of damaged buildings are essential after destructive earthquakes. However, the accuracy of image change detection is limited because of the many texture features and changes in non-building information. In this context, a model for single-building damage detection based on multi-feature fusion is proposed. First, the normalized Digital Surface Model (nDSM) was extracted from the DSM through iterative filtering and point cloud thinning, followed by the extraction of building contour information. Next, single-building images were generated from different data sources through the region of interest (ROI), and the optimal texture feature parameters were extracted for fusion. Afterward, principal-component analysis (PCA) was conducted to suppress multi-feature correlation-induced information redundancy. Finally, the damage to buildings was quantitatively evaluated, and the model was compared with 13 models. The results confirmed the practicability of the model for the Yangbi MS6.4 and Honghe MS5.0 earthquakes.