Abstract Traffic congestion can be a critical impediment to achieving safe evacuations, especially in the case of short-notice wildfire evacuations. This paper describes a quantitative assessment of traffic movements across the island of Maui, HI, before, during, and after the devastating 2023 wildfires. Traffic counts from the Federal Highway Administration (FHWA) Traffic Monitoring System across Maui were used to reveal where, when, and how much traffic moved on the island, with a particular focus on the Lahaina area. Postdisaster assessments of wildfire events are an invaluable process to learn lessons and improve future planning, preparedness, and response efforts. In this assessment, baseline traffic patterns were established from data collected over five weeks before the fire. Then, data from three weeks during and after the fire were used to identify, quantify, and illustrate patterns of evacuation and reentry. Among the key findings of the work was the difficulty in revealing clear and indisputable patterns of evacuation or reentry out of or into the Lahaina area. This is uniquely different from similar large-scale evacuations in the past, where clear volume patterns typically emerge that show times, directions, and amounts of vehicle movement. It also suggests that evacuees did not travel far, did not travel at all, or moved by modes other than by vehicle in significant numbers. This lack of definitive evidence is both important and useful because, despite more than 100 fatalities and unprecedented levels of destruction, it suggests (1) highly congested and blocked roadway conditions; (2) low situational awareness to make decisions in sufficient time; and/or (3) highly localized transportation impacts. The study also indicates how other data sources need to be considered to capture wildfire evacuation processes to improve infrastructure, operations, and planning.
Abstract Flooding poses an escalating challenge for Australia as climate change and urban expansion increase exposure to high-impact events. Structural defenses such as levees and dams remain central to national resilience, yet their economic justification depends on a robust cost–benefit analysis (CBA). This study examines how CBA is applied to flood protection projects in Australia through two contrasting case studies: (1) operational optimization of the Wivenhoe–Somerset Dam system in Brisbane; and (2) regional levee upgrades in Wagga Wagga, New South Wales. Both analyses demonstrate that a CBA provides a systematic framework for assessing investment efficiency, but that outcomes are highly sensitive to assumptions about flood probabilities, discount rates, and nonmarket benefits. The results reveal three enduring challenges: (1) limited valuation of intangible and social benefits, (2) uncertainty in design floods under climate change, and (3) trade-offs between safety, water supply, and mitigation performance. Drawing on these insights, the paper proposes a climate-adaptive CBA framework that integrates climate-scenario modeling, valuation of social and environmental co-benefits, and system-level optimization. The framework provides a pathway toward a nationally consistent, resilience-focused approach to flood-risk investment, ensuring that future flood-defense decisions in Australia are economically justified, socially equitable, and adaptable under a changing climate.
Abstract Disaster preparedness efforts often fail not because forecasts are inaccurate, but because warnings do not reach or are not understood by those at risk. Hurricane Katrina exemplifies how communication breakdowns can turn accurate forecasts into catastrophic losses, while the 2025 flash flooding in Texas demonstrates that monitoring systems alone are insufficient without timely, comprehensible information. This study evaluates the usability of Delaware’s Coastal Flood Monitoring System (CFMS) in a framed field economic experiment that integrates eye-tracking technology. We compare the comprehension and performance of experts and the public across eight incentivized tasks, analyzing both traditional usability metrics (accuracy) and process-level eye-tracking indicators (fixations, saccades, fixation duration). The public had a lower accuracy rate and required more fixations, longer fixation durations, and more saccades to complete tasks, whereas experts achieved higher accuracy and processed information more efficiently with significantly fewer fixations, shorter durations, and fewer saccades. Heatmap analysis revealed the largest usability gap in flood map tasks involving submerged roads (a potential life-threatening risk): the public’s visual attention was scattered, whereas experts’ fixations were concentrated, indicating clearer task comprehension. These findings show that some CFMS elements remain difficult for the public to interpret, highlighting the need for user-centered design alongside technical accuracy. Strengthening accessibility is essential if early warning systems are to achieve their life-saving potential as flooding risks intensify.
Abstract This study investigated the diverse effects of earthquakes, particularly on the education system, students, and teachers, drawing on the firsthand experiences of 53 volunteer teachers (VTs) during the postearthquake period, with a focus on their specific educational efforts, challenges, and emotional states. The findings revealed that the greatest challenge for VTs during the postearthquake period was psychological disorders and related concerns; VTs employed various strategies, such as adopting psychologically supportive attitudes, mutual sharing, and spending more time with students through social activities, drama, and games, to help students recover from poor mental health. Furthermore, a specific strategic plan and an educational disaster pedagogy are deemed essential in postearthquake situations to guide teaching.
Abstract In earthquake-prone regions worldwide, enhancing residential seismic resilience is a critical issue, particularly in areas with aging infrastructure. Encouraging public participation in seismic evaluation and retrofitting is essential for disaster risk reduction. However, complete seismic retrofitting or urban renewal involves high costs and complex coordination among property owners, often resulting in prolonged decision-making processes. To mitigate risks during this transitional phase, some governments have promoted interim seismic retrofitting; however, its adoption rate remains limited, as observed in Taiwan. Therefore, to increase the individual’s willingness to adopt seismic retrofitting and reduce the impact of earthquakes, this study addresses the need to understand the factors influencing individuals’ willingness to adopt seismic retrofitting, using Taiwan as a case study and applying the Extended Theory of Planned Behavior as the analytical framework. A questionnaire was administered, and the data were analyzed using structural equation modeling, supplemented by one-way ANOVA, qualitative analysis, and expert interviews. The results indicate that risk perception, subjective norms, and perceived behavioral control are key drivers of participation, while community engagement plays a secondary role. The qualitative analysis further reveals practical barriers in real-world implementation. In addition, the study identifies the forms of support citizens expect when undergoing seismic evaluation or retrofitting. Based on these findings, the study offers policy recommendations from both economic and social perspectives to improve governmental strategies for promoting seismic retrofitting, particularly interim measures.
Abstract Expanding existing landfills offers a more economical and practical solution than constructing new disposal sites, especially in urban regions where land availability is limited. This study investigates the seismic stability of vertically expanded side-hill-type waste landfills while accounting for the combined effects of hydrostatic and hydrodynamic forces generated under six different leachate buildup conditions. Waste heterogeneity is incorporated by considering variations in unit weight with depth and changes in the pore water pressure ratio, ensuring a realistic representation of field waste behavior. Two translational failure modes, under-berm and over-berm failure, are evaluated using composite failure surfaces that develop through the waste mass. The closed-form solution indicates that the expanded landfill can safely withstand a horizontal seismic acceleration of 0.16 g. The leachate buildup that is horizontal to the landfill cell and parallel to the landfill back slope is identified as the most critical scenario. It is observed that when the internal friction angle of the waste decreases from 30° to 25°, the critical slip surface shifts from following the liner to passing through the waste, which has implications for liner material selection. A shift in the governing failure condition from over berm to under berm is observed when the berm back slope becomes steeper than the intersection point. The maximum safe expansion height of the landfill is determined to be 96 m for defined parameters.
Abstract Earthquake-induced landslides are a major natural hazard and have attracted extensive global attention. Using 9,160 articles retrieved from the Web of Science Core Collection, we screened 6,956 publications highly relevant to earthquake-induced landslides through Python-based processing and manual verification. We combined VOSviewer with a latent Dirichlet allocation (LDA) topic model to quantify publication output, map keyword networks, delineate thematic structure, and trace research hotspot evolution. Publication trends exhibit three stages: an initial stage (1972–1999), a steady-growth stage (2000–2008), and a rapid-growth stage (2009–2024). The global research landscape has shifted from early dominance by Europe and North America to a multipolar pattern, with China emerging as a leading contributor while the United States, Italy, and Japan remain consistently productive. Research hotspots evolved from fundamental geological issues and slope-stability mechanisms to the widespread use of GIS and remote sensing for landslide inventory construction, spatial mapping, and hazard monitoring, followed by rapid growth in numerical modeling and machine learning. The LDA results cluster the literature into six research directions, and topic similarity analysis suggests that “mechanism studies” are most tightly coupled with “dynamic modeling,” whereas “cascading hazard impact assessment” is relatively independent. Overall, future research in this field is likely to emphasize (1) developing high-quality landslide inventories; (2) improving model transferability across events and regions; and (3) promoting application-oriented translation and decision support for risk governance.
Abstract Under the influence of global warming, the frequency of rainstorm disaster chains continues to increase with more severe impacts, posing substantial threats to socioeconomic development and public safety. Early identification and prevention of rainstorm disaster chains are crucial for reducing disaster losses. To address this challenge, this study introduces an event evolutionary graph–based framework for risk identification and resilience enhancement of such disaster chains. The framework involves three key steps. First, an event evolutionary graph–based risk network for rainstorm disaster chains is constructed by extracting causal relationships from historical disaster data. Next, a historically calibrated topological metric is developed to identify critical risk nodes and high-risk propagation pathways. Finally, the proposed method was validated using a case study of Sichuan Province by comparing the effectiveness of resilience enhancement strategies guided by different risk identification approaches. The results demonstrate that the proposed event evolutionary graph–driven framework significantly outperforms conventional methods in both accurately identifying critical risk nodes and enhancing systemic resilience. This study thus provides a scientific basis for formulating targeted prevention strategies and offers effective decision support for improving risk management of the rainstorm disaster chain.
Abstract Hurricanes pose increasingly severe threats to rural communities, where recovery faces unique challenges due to geographic isolation, limited infrastructure, and socioeconomic vulnerabilities that amplify postdisaster impacts. Although extensive literature documents numerous barriers to rural recovery, critical gaps persist in understanding how these barriers interact and which should be prioritized. This study employs a mixed-method approach integrating systematic literature review, expert validation, and social network analysis (SNA) to identify, classify, and prioritize 52 barriers affecting posthurricane recovery in rural communities. Through a comprehensive analysis of 171 publications complemented by expert interviews, barriers were categorized into 10 attributes. The reviewed publications were further organized into four document categories: detailed discussions, qualitative analyses, frameworks, and mathematical models. Reference and adjacency matrices were developed for each category, enabling computation of centrality measures, primarily degree of centrality, supplemented by closeness, betweenness, and eigenvector metrics, to quantify barrier interconnectedness and influence. Results revealed that gender and minority discrimination, unclear or weak policies, lack of proper planning and preparedness, lack of education, and lack of income sources emerge as consistently central barriers across all analytical categories. This study concluded with a seven-level hierarchical conceptual framework that systematically prioritizes barriers based on their centrality and interconnectedness. The findings of this study provide actionable insights for decision makers by identifying the most influential and interconnected barriers affecting rural posthurricane recovery and support strategic resource allocation, rural-focused policy development, and timely recovery procedure.
Abstract Near-fault pulse-like ground motions (GMs), characterized by large-amplitude coherent velocity pulses, have been demonstrated to exhibit a stronger correlation with landslide occurrence than ordinary non-pulse-like GMs. Therefore, comprehensive research on seismic slope fragility is crucial in enhancing earthquake resistance and disaster reduction capabilities in near-fault areas. This study proposes an approach based on incremental dynamic analysis (IDA) to develop fragility functions with multiple vulnerability states for the slope, using both the factor of safety (FOS) and Newmark sliding displacement ( D N ) as damage indices. In IDA, a total of 28 high-quality ground-motion records, including 14 pulse-like and 14 non-pulse-like GMs, were selected and scaled by peak ground acceleration (PGA) to multiple intensity levels ranging from 0 to 0.6 g. The FOS and D N of the slope, along with the corresponding PGA and peak ground velocity, are extracted to construct IDA. Ultimately, the exceedance probabilities of different slope limit states under given pulse-like and non-pulse-like ground-motion intensities are obtained. The results demonstrate that it is essential to incorporate pulse-like GMs and conduct two-dimensional (2D) fragility analysis, as incorporating pulse-like GMs yields higher exceedance probabilities than non-pulse-like GMs at the same IM, and 2D fragility curves similarly produce higher probabilities compared with one-dimensional curves. An illustrative example is provided to demonstrate the application of the proposed approach in seismic slope fragility analysis.
Abstract Floods pose growing risks to vulnerable areas due to climate variability and change and rapid urbanization, making resilience an essential approach for living with recurrent flood events. Developing a flood-resilience index is challenging due to the numerous uncertainties involved, but it is vital for assessing a region’s capacity to withstand floods. This study constructed a flood-resilience index using 29 indicators across 5 dimensions: natural, physical, social, economic, and institutional. Data were collected through household surveys, interviews, and hydraulic modeling in three flood-prone Grama Niladari (GN) divisions in Sri Lanka: Megoda Kolonnawa, Pahala Bomiriya, and Kerawalapitiya. The results show that applying locally tailored resilience interventions, such as flood insurance, elevated housing, and sponge city strategies, substantially improved resilience scores: from 0.495 to 0.557 in Mgoda Kolonnawa, from 0.489 to 0.573 in Pahala Bomiriya, and from 0.485 to 0.569 in Kerawalapitiya. The proposed framework supports the selection of effective interventions for building flood-resilient communities, and was validated in the lower Kelani River Basin of Sri Lanka. These interventions are broadly relevant to developing countries, in which communities already are adopting similar measures to enhance their resilience to floods. Overall, the improved index scores confirm the framework’s value in evaluating resilience-building options prior to implementation.
