This scoping study integrates research on the experiences of deaf, Deaf or hard-of-hearing (DHH) people during extreme weather events and other emergencies in Organisation for Economic Co-operation and Development countries. The review identified 48 articles published between January 2005 and August 2024. Data charting and inductive analysis of these articles identified three factors that influence access to and participation in communication during emergencies: (1) emergency warnings, alerts, and announcements; (2) the emergency sector response; and (3) emergency preparedness. There are opportunities to leverage capabilities and facilitators to address common barriers and support increased accessibility and inclusion for DHH people before, during, and after emergencies. These include ensuring that warnings and alerts are provided in multiple formats and channels; providing training for first responders in communicating with DHH people; including the diverse needs of DHH people in emergency plans at all levels; and ensuring that training and educational materials are accessible and relevant to DHH people. There are roles for DHH community organizations and the hearing care sector to link DHH individuals with information, education, and training provided by the emergency sector. For many DHH people who do not have connections with support organizations or other DHH people, the hearing care sector may be the only linkage point between their deafness or hearing difficulties, their hearing devices, and their personal emergency awareness and preparedness.

In recent years, digital devices became crucial for residents to participate in flood emergency response, as it enables rapid information flow, resource mobilization, and coordination, thus improving response efficiency. Yet the influence of digital devices on resident participation in flood disaster response has not been well explored. This study adapted the community capital framework (CCF) to explore how digital devices impact residents’ participation willingness during urban flood response by using the gradient boosting decision tree (GBDT) model. Data were collected from 1,351 respondents in Zhengzhou City, China, in 2023. The study revealed that: (1) Digital devices significantly influence residents’ participation willingness in flood response, especially in terms of information acquisition and sharing. (2) Indicators of digital devices, such as their usefulness and residents’ proficiency in using them, are key predictors of participation willingness, and their relationships are primarily nonlinear. (3) Digital devices demonstrate both reinforcement and compensation patterns in the interaction effects on residents’ participation willingness. Interaction mechanisms underscore the pivotal role of digital technologies in participatory flood emergency response. These findings can help stakeholders better assess the benefits of enhancing digital device support in flood emergency responses.

This study mapped the knowledge structure and examined the knowledge linkage in U.S. emergency management research from 2011 to 2024. Drawing on a conceptual framework that categorizes knowledge into scenarios, missions, and influencing factors, we constructed a dataset of publications authored by scholars affiliated with academic emergency management programs, retrieved from the Web of Science. Using bibliographic coupling and clustering analysis, we identified 22 research themes across five core research areas. Our findings indicate that although the field is thematically diverse, linkages between themes and areas remain limited. Nevertheless, knowledge integration has increased after 2016, with a few integrative hubs emerging. Further analysis of shared references suggested that integration occurred through shared theoretical or methodological foundations, asymmetric knowledge borrowing, and growing conceptual convergence. This study provided a comprehensive and empirical assessment of the interdisciplinary evolution of emergency management research and offered insights for scholars and academic program administrators seeking to strengthen knowledge integration and field development.

Extreme hazard events can severely threaten the well-being of society. To understand their impacts on the well-being of people, social scientists have proposed various indicators related to individuals’ suffering, and analyzed them mainly via post-disaster surveys. Social media has shown its value in capturing people’s perceptions of disasters, but few scholars have investigated individuals’ suffering levels based on social media data. Accordingly, this study used social media data and developed a hybrid model that combines machine learning classifiers and lexicon-based approaches to estimate the well-being impacts of disasters, which are measured by individuals’ physical, emotional, and social suffering levels. Six machine learning models were trained to categorize the suffering as reflected by disaster-related posts on Weibo. Convolutional neural networks (CNN) were found to be the most accurate model, and was selected to classify all posts into four groups (no suffering, physical suffering, emotional suffering, and social suffering). In each classified group of posts, word co-occurrence analysis was then applied to construct suffering lexicons. By combining the classification results from CNN and suffering lexicons, this study proposed optimization-based algorithms to estimate suffering levels for posts across space and time. The proposed model was applied to the 2023 Beijing-Tianjin-Hebei extreme rainfall event. The temporal analysis revealed that individuals’ physical suffering levels declined more rapidly than mental and social suffering levels. Spatial analysis revealed that individuals’ suffering presented high spatial heterogeneity, and that the hazard-affected regions experienced significantly greater levels of suffering. This hybrid model provides an analytical tool for timely and human-centered disaster emergency management.

Spain experienced an extreme weather event known as DANA (Cut-off Low) between late October and early November 2024. This event triggered intense rainfall, causing several rivers to overflow and severely damaging infrastructure. The Valencian Community, with over 200 fatalities, was among the most affected regions in what is considered one of the most severe hydrological disasters in the country’s contemporary history. This research aimed to understand how digital environments facilitated the organization and mobilization of citizens in response to institutional inaction during this crisis. The study’s primary objective was to analyze public reactions on social media, specifically on X, in light of the lack of effective intervention by authorities. A mixed-methods approach achieved this by combining a review of official documents and media reports with an analysis of social media posts. The main conclusion drawn from this study is that social media interactions during this emergency fostered emotional, social, and organizational bonds in the initial days of the crisis. These connections aimed to safeguard citizens’ well-being through the aid offered by individual platform users as part of spontaneous collective action.

Emergency management requires efficient evacuation planning and the delivery of rescue supplies within dynamic road networks disrupted by ongoing disasters. Two critical challenges arise: (1) determining appropriate origin-destination (OD) assignments; and (2) identifying optimal paths among multiple OD pairs in real time. However, traditional static path optimization (SPO) and dynamic path optimization (DPO) often fall short in adapting to rapidly evolving conditions, risking failure in emergency response. To address these limitations, we proposed a novel method by modifying the co-evolutionary path optimization (CEPO) based on the ripple spreading algorithm (RSA), which can simultaneously determine optimal OD pairs and corresponding paths in a single run, even under dynamic disaster environment. The effectiveness and advantages of the method are verified by comprehensive experiments.

Supply–demand allocation is important for supporting emergency food material management and decision making. This study proposed a supply–demand allocation method for market-supplied materials. The method considers the constraint that market supply reserve depots (MSDs) need to preferentially supply emergency food materials to original demand points, which is mostly neglected in traditional methods. The constraint enables the method to provide a more rational allocation scheme of MSDs. Based on the supply–demand allocation method, an emergency material distribution path planning method under flood scenarios was further developed. Unlike the traditional methods, which mostly neglect simultaneous consideration of the travel time and path reliability factors, this method comprehensively achieves two critical objectives: the shortest path travel time and highest path reliability. The heuristic algorithms are used to solve the optimal path. It can enhance the safety and reliability of food material distributions. Three criteria—degree, squares clustering coefficient, and road design daily traffic volume—are integrated to evaluate the reliability of each road section based on the real road networks, and the impact of the flood on travel time is fully considered. A case study in Fengxian District, Shanghai, China, was conducted to demonstrate the feasibility of the method. Three categories of supplies—rice, drinking water, and infant milk—were chosen to represent the food supplies. The results of the case study can support decision making for emergency rescue and relief efforts of relevant government departments. The methods proposed provide methodological references for related studies in other similar regions.

The fusion of remote sensing and social media data has shown great potential in urban flood risk assessment. However, most related studies seldom consider the loss of time information and leverage social media data gathered throughout the disaster period for spatial information enhancement. Meanwhile, existing models for correcting the spatial bias between remote sensing and social media data rely on prior flood information, which is generally unavailable in countries and regions lacking urban flood monitoring infrastructure. In this study, we first combined spatiotemporal fusion of remote sensing data and feature extraction to obtain the flood prior distribution, which was used to extend the geographic optimal transport (GOT) model for scenarios with limited prior information. We then developed a morphology-based spatiotemporal enhancement method to fuse remote sensing features-derived information and relocated social media data for time-series flood risk assessment. Taking the 2016 Wuhan flood event as a case, our study showed that the precision of the relocated Weibo data reaches 82.41%, which greatly reduces the location uncertainty of the original data. In addition, the relocated Weibo data can help identify the flooded areas that may be ignored by remote sensing-based results and significantly increase the estimated flooding probability of ground-truthing derived flooded points at a local scale. Furthermore, the time-series flood risk maps at a 2-day interval exhibit good consistency with the real flooding situation. Overall, the proposed method shows potential to compensate for the loss of time information and realize time-series flood risk assessment based on data fusion.

Urban flooding induced by heavy rainfall is increasingly frequent, necessitating accurate and timely flood forecasting to mitigate risks. Although data-driven models have demonstrated significant potential for real-time flood prediction due to their computational efficiency, current implementations frequently neglect the critical influence of rainfall spatial heterogeneity, resulting in inaccuracies in flood prediction. Therefore, this study designed diverse rainfall scenarios featuring moving rainstorm centers and proposed a fast simulation method for urban flooding under complex rainfall conditions, utilizing the convolutional long short-term memory (ConvLSTM) model. The efficacy of the proposed method was validated across three study areas. The results indicate that the ConvLSTM model has superior performance in predicting flood inundation depth and extent, achieving an average R² of 0.964, outperforming two other deep learning models. Notably, this model achieved predictions within seconds based on input rainfall data, offering high computational efficiency that is hundreds of times faster than hydrological–hydrodynamic coupled models. Furthermore, we explored the model’s extrapolation capability when rainfall intensities exceed the maximum value of the training set. This research contributes insights to the advancement and refinement of rapid urban flood forecasting methodologies.

The rapid and accurate assessment of severely affected areas following an earthquake is essential for effective emergency rescue. Mobile signaling data, with their strong responsiveness to earthquake impacts, represent a valuable tool for swiftly identifying these areas. This study employed comparative analysis, spatial interpolation, and random forest regression to develop an approach for the rapid assessment of severely affected areas using multiple mobile signaling indicators and multi-temporal analysis. Considering the 2023 Jishishan Ms 6.2 earthquake as an example, this study examined the spatiotemporal dynamics of mobile signaling variations before and after the earthquake. Then the severely affected areas of Jishishan earthquake were identified using the proposed assessment method, with results cross-validated against observed earthquake damage to confirm the method’s reliability. The findings revealed substantial anomalies in mobile signaling data post-earthquake, with varying degrees of responsiveness observed across different seismic intensity zones and indicators. These anomalies were strongly correlated with the actual extent of damage in the affected regions. Additionally, random forest regression was applied to develop seismic intensity evaluation models using mobile signaling data and epicentral distances, further refining the identification of severely impacted zones. In conclusion, the earthquake impact assessment approach using mobile signaling data represents a promising method for the swift and precise identification of severely affected areas. This approach offers a crucial support for post-earthquake emergency response, enhancing both the efficiency and effectiveness of disaster relief efforts.