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Earthquake-induced rock landslides in the eastern mountains of the Tibetan Plateau, especially landslides with weak interlayers pose a significant threat to major construction projects. Prestressed anchor cable is one of the main reinforcement methods of rock slopes. This paper combines shaking table model tests and numerical simulation to study the reinforcement effect and dynamic response characteristics of prestressed anchor cables applied to rock slopes with weak interlayers under strong earthquakes. The research results show that prestressed anchor cables can effectively reinforce slopes with weak interlayers. A small cable inclination, a small spacing and a high prestress are recommended in the seismic reinforcement design of prestressed anchor cable. In addition, the characteristics of slope progressive damage and prestress loss under the earthquake are found by the shaking table test. The results have been applied in hazard prevention and control of rock slopes on the Chengdu-Lanzhou Railway at the eastern Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

Introduction: One form of effort to reduce the impact of disasters is to improve disaster mitigation. To minimize the risk and impact of disasters, disaster mitigation is needed. One form of effort from disaster mitigation is to form disaster preparedness. There are factors to to improve disaster preparedness. This study aims to identify the factors that influence community preparedness in facing disasters. Materials and methods: This study was correlational and using proportional random sampling, the sample consisted of 300 people. The instrument used in this study was a self-administered questionnaire. Instrument making refers to the Likert scale and Guttman scale. The validity test for all variable instruments had p < 0.05 and reliability in the range of 0.6-0.8. The statistical analysis used was path analysis. Results: Disaster preparedness was directly influenced by social capital (b=0,257, p=0,000), social cohesion (b=0,202, p=0,002), social support (b=0,238 p=0,000), community resilience (b=0,170, p=0,003). Disaster preparedness is indirectly influenced by social capital through social cohesion, social support, and community resilience. Social cohesion indirectly influences disaster preparedness through social support and community resilience. Conclusion: Social capital, social cohesion, social support, and community resilience can improve community disaster preparedness. [ABSTRACT FROM AUTHOR]

To understand the development status and frontier trends in the prevention and control of coal mine water hazards, a dataset of 962 articles indexed in Web of Science was used. Visualization maps were created using CiteSpace software to conduct statistical analyses on the publication volume, journal distribution, researchers, research institutions, keywords, and burst terms in the field of coal mine water hazard prevention and control in China from 2000 to 2023. The study reveals that the number of publications related to the prevention and control of coal mine water hazards in China has gone through rapid growth periods as well as stable fluctuations. The literature cooperation network in the field of coal mine water hazard prevention and control consists of two main collaboration groups and a series of independent authors, with the core research strength concentrated in institutions such as China University of Mining and Technology, Shandong University of Science and Technology, China University of Mining and Technology (Beijing), and Xi'an Research Institute of China Coal Technology and Engineering Group. Keywords such as "Coal mine water hazards," "Water inrush," "numerical simulation," "aquifer," and "risk assessment" collectively form larger clusters, primarily revolving around three main research directions: "water source," "water inflow pathways," and "water inflow intensity." In-depth exploration of the mechanisms of water inrush in coal mines, the revelation of water-conducting fracture evolution patterns, understanding underground water seepage characteristics, and hydraulic characteristics, along with the extensive use of new technologies like artificial intelligence and big data, will be the future hotspots in research. [ABSTRACT FROM AUTHOR]

Illegal coal mining is prevalent worldwide, leading to extensive ground subsidence and land collapse. It is crucial to define the location and spatial dimensions of these areas for the efficient prevention of the induced hazards. Conventional methods for goaf locating using the InSAR technique are mostly based on the probability integral model (PIM). However, The PIM requires detailed mining information to preset model parameters and does not account for the layered structure of the coal overburden, making it challenging to detect underground goaves in cases of illegal mining. In response, a novel method based on the InSAR technique and the Stratified Optimal Okada Dislocation Model, named S-ODM, is proposed for locating goaves with basic geological information. Firstly, the S-ODM employs a numerical model to establish a nonlinear function between the goaf parameters and InSAR-derived ground deformation. Then, in order to mitigate the influence of nearby mining activities, the goaf azimuth angle is estimated using the textures and trends of the InSAR-derived deformation time series. Finally, the goaf's dimensions and location are estimated by the genetic algorithm–particle swarm optimization (GA-PSO). The effectiveness of the proposed method is validated using both simulation and real data, demonstrating average relative errors of 6.29% and 7.37%, respectively. Compared with the PIM and ODM, the proposed S-ODM shows improvements of 19.48% and 52.46% in geometric parameters. Additionally, the errors introduced by GA-PSO and the influence of ground deformation monitoring errors are discussed in this study. [ABSTRACT FROM AUTHOR]

Expert perspectives drive landslide mitigation and post-disaster policy planning. This study examines landslide risk perceptions among the stakeholders (government officials, academics, policy experts, local community representatives, and representatives of NGOs/civil society) across Brazil, Colombia, Nepal, Iran, and Pakistan, identifying both shared concerns and local heterogeneity. Key informants revealed a discrepancy in their degree of concern about landslides, with government officials exhibiting greater apprehension compared to local community representatives. Local community representatives incorrectly perceived landslides to be the result of natural phenomena. In contrast, governmental and academic stakeholders felt that human-induced triggers, specifically those related to land use and land cover change, were significant contributors to landslide occurrences, necessitating stringent law enforcement. The comprehensive impacts of landslides included economic losses, infrastructure disruption, agricultural losses, and food security concerns, underscoring the multifaceted nature of this hazard. Our results suggest the need for proactive citizen engagement in landslide monitoring, recognizing the importance of local contexts. We end by proposing a dual-pronged policy approach that emphasizes the socio-economic context of each region. [ABSTRACT FROM AUTHOR]

Large amounts of co-seismic landslides provide abundant debris sources following a strong earthquake and are prone to initiate and generate debris flow under heavy rainfall. Assessing the susceptibility they pose and what drives their movement in the years following the mainshock has not yet been attempted, primarily because multitemporal debris flow inventories are lacking. This study conducted statistical analyses using the multitemporal debris flow inventory (2008–2021) following the 2008 Wenchuan earthquake and a set of conditional factors (debris source, terrain, and hydrology). The dynamic susceptibility evaluation model was created using logistic regression. The temporal evolution of these factors affecting debris flow runout was explained. Our findings suggest that topography variables grew rapidly after the earthquake, while debris source factors dominated for seven years after the disaster before declining progressively to zero. These findings may have significant consequences for traditional susceptibility assessment models in regions where co-seismic landslides are the dominant debris flow source. [ABSTRACT FROM AUTHOR]

Objectives: Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure. of feature maps, one-dimensional CNN（1D-CNN） and two-dimensional CNN（2D-CNN） were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multidimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility. Results: The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results. Conclusions: The multidimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention [ABSTRACT FROM AUTHOR]

The present paper argues that the standardised treatment of disaster research and practice perpetuates the production of systematic epistemic injustices against victims of disasters. On the one hand, disaster victims are often prevented from contributing with their opinions and knowledge to the processes of disaster mitigation and disaster conceptualisation. On the other hand, disaster victims tend to lack the hermeneutical resources to make sense of their experiences intelligibly, due to the existence of significant hermeneutical gaps in the hegemonic terminology on the matter. I argue that both forms of epistemic injustice, the testimonial and the hermeneutical, are sustained by an epistemic privilege between the Global North and the Global South in matters of disasters. The second group comprises what I categorise generally as 'disaster victims'. I identify two forms of structural prejudice that operate against disaster victims: one is the 'non-expert' prejudice, and the other is the colonial prejudice. Finally, because of the intercultural nature of disaster environments, I discuss the field of 'multicultural competencies' as a useful form of unveiling and counteracting the epistemic injustices contained in both disaster theory and practice. [ABSTRACT FROM AUTHOR]

On October 7, 2021, a magnitude 5.9 earthquake struck the Harnai (Baluchistan) region of Pakistan, causing several fatalities and injuries within the epicentral area. First-order tectonic deformation in this region is caused by the convergence of the Indian Plate with respect to the Eurasian Plate. The Katwaz Block hinders the motion of the Indian Plate, resulting in the formation of strike-slip faults. In this study, the P-wave first-motion polarity technique was used to determine the mainshock faulting style. Cyclic scanning of the polarity solutions was applied to determine the most suitable focal mechanism solution among the available solutions generated by the FOCMEC (focal mechanism) software. The nodal planes correspond to different faulting styles (i.e., thrust and strike-slip faulting). A nodal plane oriented in the NW-SE direction corresponded to a strike-slip mechanism, which was considered to be the fault plane. Tectonically, this earthquake was associated with the Harnai-Karahi strike-slip fault zone owing to the fault strike and direction of slip. The apparent stress drop, fault length, and moment magnitude of the Harnai earthquake were 35.4 bar, 6.1 km, and 5.9, respectively. A lower b-value for the Gutenberg-Richter law was observed prior to the earthquake. Higher α- than b-values (α > b) indicate that this earthquake was governed by large events as opposed to small-magnitude events. The Harnai sequence had a decay exponent close to unity, lasted for 145 days, and produced few aftershocks. The study will help the future hazard mitigation in the region. [ABSTRACT FROM AUTHOR]

Following the 2016 Chauk earthquake in Bagan (Myanmar), numerous old pagodas and temples suffered severe damage. This research presents a study on the seismic hazard analysis of the Bagan city in Myanmar, based on a probabilistic framework focussing on analysing 43 temples with their associated local soil information. To this end, two seismic source models are developed based on the tectonic setting of the region and information available. Instrumental and historical records are compiled from both literature and international earthquake catalogues while conducting catalogue completeness. This study uses state-of-the-art ground motion models to perform probabilistic seismic hazard analysis and develop seismic hazard maps for different return periods in the region. Results are also expressed for selected temples in the region in terms of site-specific uniform hazard spectra. The findings indicate significant seismic activity, with peak ground acceleration in the region ranging from 0.25 to 0.36 g for a return period of 2475 years, 0.22–0.32 g for a return period of 975 years, and 0.18–0.24 g for a return period of 475 years. The updated hazard levels indicate that the literature slightly underestimates hazard in the region under study. [ABSTRACT FROM AUTHOR]

This study aims to identify what are the coastal protection strategies and identify the impacts of abrasion disasters on the region. In addition, this study also wants to ensure that mitigation efforts not only focus on physical protection, but also consider the sustainability of ecosystems and the well-being of coastal communities. In addition, this study also wants to ensure that mitigation efforts not only focus on physical protection, but also consider the sustainability of ecosystems and the well-being of coastal communities. The research method used is literature study and secondary data analysis related to coastal protection strategies that have been implemented and their impact on abrasion mitigation. The results of the study show that the implementation of coastal protection strategies such as the construction of breakwaters, sea embankments, mangrove management, sand management, by adding sand to eroded beaches, implementing integrated management of coastal areas has had a positive impact on abrasion disaster mitigation in the region. However, challenges such as climate change and ecological imbalances need to continue to be considered in designing sustainable coastal protection strategies in the future. In conclusion, this study provides useful insights for policymakers and practitioners related to abrasion disaster mitigation efforts in the coastal area of Wonokerto, Pekalongan. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Environmental conservation and disaster mitigation still emphasize science and technology, while the potential of the socio-cultural wealth within communities remains underutilized. This shows a substantial gap, as active community involvement, rooted in socio-cultural wealth, is essential for effective environmental conservation and disaster mitigation. To address the existing gap, this research aims to identify, analyze, and interpret the values of Sundanese Indigenous laws (Pikukuh) that can be utilized and relevant for these efforts. This study used a qualitative method with a case study design in Kampung Naga, Baduy, Kuta, and Cikondang. The results showed that Pikukuh embodied important values for environmental conservation efforts. These rules played a significant role in conserving forests, improving vegetation conditions, enhancing rainwater absorption, ensuring soil stability, preventing erosion, and maintaining the groundwater cycle. These principles are crucial for mitigating natural disasters like landslides, floods, droughts, and climate change. Pikukuh principles need to be incorporated more broadly and actively applied in policies and strategic initiatives to advance sustainable development, as this goes beyond romanticizing traditions and is essential for securing a sustainable future life. [ABSTRACT FROM AUTHOR]

This study addresses the pressing need for flood extent and exposure information in data-scarce and vulnerable regions, with a specific focus on West Africa, particularly Senegal. Leveraging the Google Earth Engine (GEE) platform and integrating data from the Sentinel-1 SAR, Global Surface Water, HydroSHEDS, the Global Human Settlement Layer, and MODIS land cover type, our primary objective is to delineate the extent of flooding and compare this with flooding for a one-in-a-hundred-year flood event, offering a comprehensive assessment of exposure during the period from July to October 2022 across Senegal's 14 regions. The findings underscore a total inundation area of 2951 square kilometers, impacting 782,681 people, 238 square kilometers of urbanized area, and 21 square kilometers of farmland. Notably, August witnessed the largest flood extent, reaching 780 square kilometers, accounting for 0.40% of the country's land area. Other regions, including Saint-Louis, Ziguinchor, Fatick, and Matam, experienced varying extents of flooding, with the data for August showing a 1.34% overlap with flooding for a one-in-a-hundred-year flood event derived from hydrological and hydraulic modeling. This low percentage reveals the distinct purpose and nature of the two approaches (remote sensing and modeling), as well as their complementarity. In terms of flood exposure, October emerges as the most critical month, affecting 281,406 people (1.56% of the population). The Dakar, Diourbel, Thiès, and Saint-Louis regions bore substantial impacts, affecting 437,025; 171,537; 115,552; and 77,501 people, respectively. These findings emphasize the imperative for comprehensive disaster preparation and mitigation efforts. This study provides a crucial national-scale perspective to guide Senegal's authorities in formulating effective flood management, intervention, and adaptation strategies. [ABSTRACT FROM AUTHOR]

Objective: To investigate the impact of extreme climate on geological disasters in Shanxi and propose effective disaster prevention and mitigation strategies. Methods: Using daily temperature and precipitation data from 27 meteorological stations in Shanxi Province from 1975 to 2020, 32 extreme climate indices were calculated. Combined with geological disaster site data, the distribution characteristics of extreme climates and their relationship with geological disasters were analyzed, and a regression model for geological disaster risk zones was constructed. Results: Sixteen extreme climate indices in Shanxi Province showed significant changes, especially TMAXmean (100% significant). Indices related to negative precipitation effects showed a declining trend, with 77.78% being significant, while 96.3% of positive temperature effect indices showed an increasing trend, with 73.6% being significant. Geological disaster hotspots were concentrated in the mid-altitude (500–1500 m) hilly and low mountain areas along the central north–south axis and on Q and Pz strata. Extreme high-temperature indices were significantly positively correlated with geological disaster hotspots, while extreme low-temperature indices were negatively correlated. Indices related to extreme heavy precipitation (e.g., R99p.Slope, RX5day.Slope) were associated with an increase in geological disaster hotspots, whereas higher total precipitation and frequent heavy precipitation events were associated with a decrease in disaster hotspots. The grey relational degree between the Z-score and TXn.Slope, TXx.Slope, GSL.Slope, and TX90P.Slope was greater than 0.8. The random forest model performed best in evaluation metrics such as MAE, RMSE, and R2. Conclusions: Shanxi is likely to experience more extreme high-temperature and precipitation events in the future. The low-altitude hilly and terraced areas in Zones III and VII are key regions for geological disaster prevention and control. High temperatures and extreme rainfall events generally increase the disaster risk, while higher total precipitation reduces it. The random forest model is the optimal tool for predicting geological disaster risks in Shanxi Province. [ABSTRACT FROM AUTHOR]

Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature selection. This study investigates the impact of various feature selection techniques on the performance of ML algorithms for landslide displacement prediction. The Shuping and Baishuihe landslides in China's Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent ML algorithms. Both static ML models, including backpropagation neural network (BPNN), support vector machine (SVM), and dynamic models, such as long short-term memory (LSTM), and gated recurrent unit (GRU), are included. Each ML model is evaluated under three feature engineering techniques: raw multivariate time series, and feature selection under maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). The results demonstrate that appropriate feature selection methods could significantly improve the performance of static ML models. In contrast, dynamic models effectively leverage inherent capabilities in capturing temporal dynamics within raw multivariate time series, seeing marginal gains with extensive feature engineering compared to no feature selection strategy. The optimal feature selection approach varies based on the ML model and specific landslide, highlighting the importance of case-specific assessments. The findings in this study offer guidance on integrating feature selection techniques with different machine learning models to maximize the robustness and generalizability of data-driven landslide displacement prediction frameworks. [ABSTRACT FROM AUTHOR]

Occurrence of natural disaster can never be prevented, and floods are one among the major natural disaster that affects the human life and economy of the country. Considering the global loss due to floods, various government and non-governmental organisations are focusing on minimising the losses and provide emergency measures during floods. Adopting the recent technologies integrating various datasets will assist in providing response strategies before and after disaster. Flood are mostly based on the climatic conditions and environmental factors and the present review focuses on the reviewing the various remote sensing methodologies that are used in mapping and analysing floods. The review carried out examines various remote sensing methodologies adopting multispectral, light detection and ranging and radar datasets for mapping and predicting floods. The review identified the limitations in flood prediction, risk assessment and hazard analysis and suggests a framework that can be adopted for effective mapping of flood extent and in suggesting the regions that the rescue team should focus during a disaster event. [ABSTRACT FROM AUTHOR]

In recent decades, the increasing frequency of urban fires, driven by urban functional enhancements and climate change, has posed a growing threat to metropolitan sustainability. This study investigates the temporal and spatial characteristics of fire incidents in Shanghai from 2019 to 2023. Using satellite fire point data and official government records, kernel density analysis and wavelet analysis were employed to analyze the time series and spatial distribution of fire data. Subsequently, eleven primary factors influencing urban fire occurrence were identified, encompassing probability, regional characteristics, and hazard sources. A combined methodology of subjective and objective weights with game theory was used to generate a fire risk assessment at a 1 × 1 km2 grid scale. Furthermore, the spatial distribution characteristics of the assessments were analyzed. The results reveal that the downtown area exhibits the highest intensity of urban fires in terms of spatial domain, with a decreasing intensity towards the suburbs. Temporally, fire frequency demonstrates significant periodicity at an 18a time scale, while clear seasonal fluctuations and periodicity are observed at a 16-22a time scale, with higher occurrences in spring and winter. The study identifies typical aggregation patterns of urban fires, with high-risk centers in downtown Shanghai. Considering the impact of climate change and human activities, high-risk areas may gradually expand to adjacent urban suburbs, presenting a concerning future scenario. By examining the dual attributes of "combustibles and fireproof space" within urban greening systems, this research offers recommendations for the future strategies of disaster prevention and mitigation of green systems in Shanghai. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geomechanics is the property of Journal of Geomechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geomechanics is the property of Journal of Geomechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Ecological Environment Code, as a significant legal instrument for the evolution of ecological civilization, possesses both substantive and formal dimensions. First, it aims to serve the national ecological civilization strategy. Second, it elucidates potential pathways for legal interpretation, development of legal rules, and promotion of judicial governance, restraining arbitrary behavior and disorder while facilitating systemic cohesion. The compilation of the Ecological Environment Code unfolds through the main threads of substantive and formal logic. By prioritizing conservation, protection, and restoring the natural environment, resource management, pollution prevention, and ecological protection are coordinated, integrating legislation on ecological protection with management of natural resources and pollution prevention to achieve both "harm avoidance" and "beneficial trends." Employing the formal logical main thread of the structure of the relationship between power and rights, we present four types of norms: ex post facto remedy, hazard prevention, risk prevention, and independent value selection. [ABSTRACT FROM AUTHOR]

China's rural regions grapple with considerable challenges concerning infrastructure and disaster prevention capabilities. Addressing these will be pivotal in the nation's future endeavors toward disaster mitigation and development. This research categorizes China's comprehensive rural disaster reduction policies (RCDRPs) spanning from 1949 to 2021 into distinct evolutionary phases, using quantitative characteristics. Utilizing sociotechnical configuration analysis, we visually chart the progression of policy focal points and draw preliminary lessons from them. Key insights include: (1) the RCDRPs from 1949 to 2021 can be segregated into three distinct phases; (2) there's been a noticeable rise in the RCDRPs jointly promulgated by multiple governmental departments, and the interconnectedness with policymaker networks has thickened, indicating increased synergy amongst governmental bodies; and (3) the primary thrust of RCDRPs has transitioned from postdisaster recuperation to predisaster safeguarding and from a primary emphasis on disaster management to a broader approach to holistic disaster reduction. Drawing upon these insights, this study employs the crisis management framework—encompassing prevention, preparation, response, and recovery—to shed light on potential directions of future disaster prevention and mitigation policies in China. The aim is to align these with the overarching vision of urban–rural integrated development, thereby advancing emergency governance modernization in China's countryside. [ABSTRACT FROM AUTHOR]

This paper proposes a flood vulnerability index utilizing publicly available data that can be applied to different spatial scales. The index enables the development of an appropriate flood risk management strategy, tailored to those factors of vulnerability that have the greatest impact on the overall flood vulnerability of a given place. Currently, following a shift in the approach to flood protection, vulnerability has become a critical element of disaster mitigation. This index is an important contribution to the existing body of research as it can be applied to flood risk management strategies overcoming the ambiguity of definitions of vulnerability and the dynamic nature of temporal and spatial scales of analysis. The proposed flood vulnerability index is a multifaceted approach, encompassing physical, social, economic, environmental, and institutional attributes. In order to create the index, the literature on flood vulnerability was reviewed, including definitions, research methods, as well as the factors and constituents that are major components of vulnerability. The proposed indicator can be used by policy-makers responsible for flood risk management, especially in EU countries, in order to minimize the negative consequences of floods. Compared to other methods used (i.e., disaster loss estimation or modeling), the flood vulnerability index is easy to use when comparing different areas. [ABSTRACT FROM AUTHOR]

Flood mitigation behavior is essential for effective flood risk management, particularly in Australia, where the federal government has increasingly emphasized individual responsibility for preventing and preparing for flood disasters. Despite this emphasis, around 60% of flood-prone residents hesitate to adopt private mitigation measures. Their reluctance highlights the complexities of decision making regarding the implementation mitigation measures, emphasizing the need for a comprehensive understanding of flood mitigation behavior among Australian residents. In order to address this knowledge gap, we conducted household surveys in flood-prone regions of Australia and used six machine learning algorithms—logistic regression, k -nearest neighbor, support vector machine, random forest, extreme gradient boosting, and artificial neural network—to analyze the proposed framework for flood mitigation behavior. The results of five-fold cross-validation demonstrated that the random forest algorithm provided superior accuracy for predicting protection motivation compared to the other five algorithms. Furthermore, seven of the 37 predictors utilized in the model had a higher impact on protection motivation than the other predictors. These influential predictors included self-efficacy, response efficacy, response costs, key variables within the coping appraisal factor, fear or worry about future flood risk, one variable associated with the threat appraisal factor, past experiences of flooding inside the house linked to the prior flood experience factor, and the implementation of flood mitigation measures by individuals connected to the householder, such as family, friends, and neighbors, which pertains to the social environment factor within the proposed framework for flood mitigation behavior. These predictors were deemed adequate to be used as input variables in the random forest model, because they provided accuracy and performance similar to when all 37 predictors were included. These insights into influential predictors can be used to develop an optimal prediction model for protection motivation in flood-prone areas of Australia and reduce the effort needed to collect the required data during postdisaster surveys. This research is of value to policymakers and floodplain managers in designing effective flood mitigation strategies and promoting the adoption of protective measures in flood-prone communities to reduce flood risk. [ABSTRACT FROM AUTHOR]

This study examines patterns of interorganizational collaboration for hazard mitigation in metropolitan regions. Metropolitan regions whose populations reside in vulnerable places may experience extreme events. Facilitation of collective actions among organizations is critical for building resilient urban communities. Although the literature highlights the importance of interorganizational collaboration in disaster management, patterns of interorganizational collaboration for hazard mitigation in urban settings has been underexamined. Building on an institutional collective action framework, this study argues that hazard mitigation efforts tend to be localized. We tested this hypothesis using survey data collected in the Busan and Ulsan metropolitan regions, where natural disasters are problematic. An exponential random graph model confirmed the presence of a bonding strategy, which builds trust with municipalities and organizations within the region and creates interorganizational collaboration. We found that local public agencies tend to collaborate with organizations in the same jurisdictions or at higher levels of government. Practical Applications: In the context of emergency management, hazard mitigation plays a crucial role in building resilient communities. Public agencies implement hazard mitigation actions by collaborating with other agencies and groups. Local agencies may require financial or logistical support from regional or national governments to effectively implement hazard mitigation actions. Agencies can foster collaborative relationships and strengthen their commitment to hazard mitigation efforts through regular interactions and by forming collaborative groups. Furthermore, collaboration among agencies within a region allows for shared interests in hazard mitigation and the strengthening of commitments. These strategies will help public agencies to implement hazard mitigation actions that utilize resources from a broad range of organizations. [ABSTRACT FROM AUTHOR]

Climate change causes frequent and severe disasters. A comprehensive assessment of disaster hazards is thus crucial to understanding variations in disaster patterns and planning mitigation and adaptation strategies. This study obtained information from a crop disaster dataset of Taiwan covering the period from 2003 to 2022. Additionally, principal component analysis and K-means clustering were used to create climate clusters to evaluate the effect of climate patterns on disaster hazards. The results revealed that tropical storm frequency substantially decreased, whereas rain disasters exhibited an increasing trend. The four regions of Taiwan exhibited variations in terms of hazards of various disasters. The cold wave hazard showed a significant upward trend in the central region. An upward trend of rain disaster hazards was only detected in the southern region. However, a downward trend in tropical storm hazards was detected across all regions. A distinct climate pattern was identified over the study period. After 2012, high temperature and dry climate were the primary climate patterns. These patterns exhibited a high hazard value for cold waves, droughts, and rain disasters. Hence, the present study's findings indicate that managing cold waves and rain disasters is crucial to protecting crop production in Taiwan. [ABSTRACT FROM AUTHOR]

Burn severity is fundamental to post‐fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics‐informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out‐of‐sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science‐based insights for managers and diverse stakeholders. Plain Language Summary: Post‐fire impact assessment and hazard mitigation heavily relies on burn severity metrics. Vegetation burn severity (VBS)—most relevant for ecological impacts—can be remotely sensed, but soil burn severity (SBS)—most relevant for hydrological impacts—requires laborious field recalibration of VBS maps. Lack of near real‐time SBS information is currently a data gap. Climate change‐driven weather whiplash can narrow the time interval between large wildfires and the ensuing precipitation events, requiring tools for rapid and accurate assessment of SBS without the need for laborious field recalibration of satellite‐derived metrics. Here we developed a physic‐informed Machine Learning model that can accurately develop SBS maps using readily available data without the need for resource and time intensive field assessments of burn severity. Additionally, SBS maps are developed for only a small fraction (less than 0.1%) of all wildfires in the US. Our model can develop SBS maps for all satellite‐observed wildfires in the western US, and has the potential to accomplish this task globally with further training. Finally, our model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. Key Points: Soil burn severity (SBS) assessment is key for hydrological hazard mitigationVegetation burn severity (VBS) can be remotely sensed, SBS is measured in the fieldPhysics‐informed Machine Learning models to translate VBS to SBS are developed [ABSTRACT FROM AUTHOR]

A novel, practical, and computationally efficient probabilistic methodology for the stochastic generation of suites of fully non‐stationary artificial accelerograms is presented. The proposed methodology ensures that the produced ground motion suites match a given target spectral mean and target variability for the whole period range of interest. This is achieved by first producing an ensemble of random target spectra with the given mean and variability and then using them to generate artificial, target spectrum‐compatible, acceleration time‐histories with spectral representation techniques. Spectral correlation can also be assumed for the generated ground motion spectra. Based on the same backbone, two different formulations are proposed for generating spectrum‐compatible acceleration time‐histories of the non‐stationary kind. The distinction between these two variants lies in the techniques employed for modeling the temporal and spectral modulation, focusing on the site‐compatibility of the produced records. The first approach uses past‐recorded seismic accelerograms as seed records, and the second proposes and uses a new, probabilistic time‐frequency modulating function. The outcome of the proposed methodology is suites containing site‐compatible ground motion time‐histories whose spectral mean and variability match those obtained from any of the usually employed target spectra used in the earthquake engineering practice. An online tool implementing the proposed methodology is also freely provided. [ABSTRACT FROM AUTHOR]

Effective volcanic impact and risk assessment underpins effective volcanic disaster risk management. Yet contemporary volcanic risk assessments face a number of challenges, including delineating hazard and impact sequences, and identifying and quantifying systemic risks. A more holistic approach to impact assessment is required, which incorporates the complex, multi-hazard nature of volcanic eruptions and the dynamic nature of vulnerability before, during and after a volcanic event. Addressing this need requires a multidisciplinary, integrated approach, involving scientists and stakeholders to co-develop decision-support tools that are scientifically credible and operationally relevant to provide a foundation for robust, evidence-based risk reduction decisions. This study presents a dynamic, longitudinal impact assessment framework for multi-phase, multi-hazard volcanic events and applies the framework to interdependent critical infrastructure networks in the Taranaki region of Aotearoa New Zealand, where Taranaki Mounga volcano has a high likelihood of producing a multi-phase explosive eruption within the next 50 years. In the framework, multi-phase scenarios temporally alternate multi-hazard footprints with risk reduction opportunities. Thus, direct and cascading impacts and any risk management actions carry through to the next phase of activity. The framework forms a testbed for more targeted mitigation and response planning and allows the investigation of optimal intervention timing for mitigation strategies during an evolving eruption. Using 'risk management' scenarios, we find the timing of mitigation intervention to be crucial in reducing disaster losses associated with volcanic activity. This is particularly apparent in indirect, systemic losses that cascade from direct damage to infrastructure assets. This novel, dynamic impact assessment approach addresses the increasing end-user need for impact-based decision-support tools that inform robust response and resilience planning. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to evaluate life safety risk of coastal communities vulnerable to seismic and tsunami hazards. The work explicitly incorporates two important aspects in tsunami evacuation modeling: (1) the effect of earthquake-induced damage to buildings on building egress time, (2) the effect of earthquake-induced debris on horizontal evacuation time. The city of Seaside, Oregon, is selected as a testbed community. The hazard is based on a megathrust earthquake and tsunami from the Cascadia Subduction Zone that was defined in a previous study. The built environment consists of buildings and the transportation network for the city. Fragility analysis is used to estimate the seismic damage to buildings and resulting debris that covers portions of the road network. The horizontal evacuation time is determined based on the shortest path to shelters, including the increased travel time due to the earthquake-generated debris. The effects of different mitigation strategies are quantified. Results indicate the fatality and life safety risk of a near-field tsunami increases by 4.2–8.3 times when the effects of building egress and earthquake-induced debris are considered. The choice of population layer affects the life safety risk and thus the maximum risk is obtained when daytime populations are considered. Use of mitigation strategies result in a significant decrease in the number of fatalities. For hazards with recurrence intervals larger than 500- to 1000-years, the seismic retrofit is comparable to vertical evacuation and an effective strategy in reducing fatalities and associated risks. Implementing all mitigation strategies reduces the life safety risk by 90%. [ABSTRACT FROM AUTHOR]

Understanding the behaviors of tropical cyclone (TC) intensity under the CO2 removal scenario is important for future climate adaptation and policy making. Based on the idealized CO2 ramp‐up (from 284.7 to 1,138.8 ppm) and symmetric ramp‐down experiments, our results suggest an asymmetric and irreversible response of TC potential intensity to CO2 reduction. Potential intensity shows an additional enhancement at the same CO2 level during the CO2 ramp‐down relative to the ramp‐up periods (though with regional differences), and does not completely return to the initial value even when CO2 recovers on multi‐decadal to centennial timescale. The enhanced potential intensity is dominated by the increased thermodynamic disequilibrium, which is mainly attributed to the weakened surface winds arising from the El Niño‐like warming pattern and inter‐hemispheric ocean temperature contrast. Our results highlight the potential risks of stronger storms on the socioeconomic development under the negative carbon emissions pathways. Plain Language Summary: Tropical cyclone (TC) is one of the most destructive natural disasters worldwide. Many studies pointed to the intensification of Tropical cyclones (TCs) in the warming future and thus it is important to limit the amplitude of global warming and mitigate the potential risks of enhanced TCs. This study examines whether TC potential intensity may return to its initial state with the CO2 removal method based on the idealized CO2 forcing experiments, in which CO2 increases from the preindustrial to quadruple level (284.7–1,138.8 ppm) and decreases symmetrically. Our results suggest an asymmetric and irreversible response of TC potential intensity to CO2 removal, with an additional enhancement at the same CO2 level during the CO2 ramp‐down relative to the ramp‐up periods. Moreover, potential intensity does not fully recover to the initial state even when CO2 is restored on multi‐decadal to centennial timescale. The increased potential intensity during the CO2 ramp‐down relative to the ramp‐up periods is dominated by the increased thermodynamic disequilibrium. This is further linked with the uneven sea surface temperature response and the resultant weakened surface wind speed. This study helps understand the response of TC activity under future mitigation pathways and highlights the potential risks of the enhanced storm intensity. Key Points: Asymmetric and irreversible response of tropical cyclone (TC) potential intensity under the symmetric CO2 rising and declining scenarioThe enhancement in TC potential intensity when CO2 recovers is dominated by the increased thermodynamic disequilibriumEl Niño‐like sea surface temperature pattern contributes to the weakened surface winds and thereby increased TC potential intensity [ABSTRACT FROM AUTHOR]

The co-occurrence of multiple hazards can either exacerbate or mitigate risks. The interrelationships between multiple hazards greatly depend on the spatiotemporal scale and can be difficult to detect from large to local scales. In this paper, we identified coastal regions worldwide where the leading tropical (El Niño-Southern Oscillation, ENSO) and polar (Arctic Oscillation, AO; Southern Annular Mode, SAM) modes of climate variability simultaneously modify the seasonal conditions of multiple hazards, including the near-surface wind speed and swell and wind-sea wave powers. We classified the results at the national and municipal levels, with a focus on multiple hazards simultaneously occurring in space and time. The results revealed that the ENSO modulates multiple hazards, affecting approximately 40% of coastal countries, while the polar annular modes affect approximately 30% of coastal countries. The ENSO induced a greater diversity of multiple hazards, with Asian countries (e.g., Indonesia experienced increases of + 2% in wind and + 7% in swell) and countries in the Americas (e.g., Peru exhibited increases of + 1.5% in wind and + 6% in wind-sea) the most notably affected. The SAM imposed a greater influence on swells in the eastern countries of ocean basins (+ 2.5% in Chile) than in other countries, while the influence of the AO was greater in Norway and the UK (+ 12% for wind-sea and 8% for swell). Low-lying islands exhibited notable variations in pairwise hazards between phases and seasons. Our results could facilitate the interpretation of multihazard interactions and pave the way for a wide range of potential implementations of different coastal industries. [ABSTRACT FROM AUTHOR]

Volcanic debris flows (lahars) are highly destructive volcanic phenomena and present significant challenges in numerical simulation. This manuscript tackles the three fundamental requirements for modelling gravitational flows: determining plausible source configurations; selecting suitable topographic data; and employing appropriate mathematical models to assess the current hazard posed by long-distance lahars at Cotopaxi volcano. After incorporating these elements, we successfully simulated the characteristics of a future 1877- type lahar under current conditions, accounting for glacier size and topography. For the source conditions, or "scenario", we identified 27 equidistant source locations along the lower edge of the current glacier's extent. Each source was assigned a hydrograph based on the weighted volume of water available on Cotopaxi's current glacier. Additionally, we introduced a methodology for quantifying channel width when high-resolution digital elevation models (DEMs) are available. This method enabled us to determine the minimum pixel size required for accurate representation of ravine shapes. While higher resolution DEMs demand robust computational resources and extended computational timeframes, we upscaled Cotopaxi's DEM from 3 m to 15 m to balance accuracy and efficiency, as a 15-m DEM capture over 90% of the topography and reduces computing time significantly. Optimizing DEM selection is crucial, especially when contemplating future ensemble approaches. After employing the dynamic-based model Kestrel, parameterised for large lahars, we obtained predictions closely aligned with field observations, historical flow conditions inferred for the 1877 lahar-event, and results from previous simulation studies. Notably, we observed higher depths and speeds in canyons compared to plains, consistent with historical reports and previous studies. Minor discrepancies in the inundation area, when compared with existing hazard maps, emphasize the importance of understanding flow dynamics and lahar trajectories for effective hazard assessment and mitigation strategies. Furthermore, our results contribute valuable information to current hazard maps and can aid in damage quantification and cost/benefit analyses, particularly when planning the construction of mitigation infrastructure. [ABSTRACT FROM AUTHOR]

Wire icing seriously threatens the safety and reliability of power systems. Strengthening the simulation study of icing thickness is crucial for disaster prevention and mitigation, as well as the adjustment of power system operation strategies. The meteorological elements, including air temperature, precipitation and wind speed, are the key factors affecting wire icing. In this study, the meteorological elements related to icing thickness are obtained by numerical models, and the relationship between these elements and icing thickness observations is established to develop a model for simulating icing thickness. The model is applied to study typical icing in Yunnan Province, China. The results indicate that the deviation of the simulated icing thickness is about 2 mm, smaller than that from the traditional model. Batch experiments demonstrate that the new model developed in this research is applicable to the vast majority of 238 observation stations in Yunnan Province for icing thickness simulation, and the method can yield lower simulation deviations. [ABSTRACT FROM AUTHOR]

Liangshan Prefecture, located at the northeastern edge of the Hengduan Mountain System and within the southern section of the Sichuan-Yunnan tectonic belt in Sichuan Province, China, a region prone to landslides, collapses and debris flows due to its active tectonics, complex topography and significant river erosion. By analysing a dataset of environment factors and geological hazard catalogue, the research uses the Relief algorithm to identify critical influencing factors for each hazard type, selecting 10, 9 and 9 factors for landslides, collapses and debris flows, respectively. Five models are used to assess the vulnerability of these hazards: the Information Value model, the Evidence Weight model, the Logistic Regression model, and both the Evidence Weight-Logistic Regression and the Information Value-Logistic Regression coupled models. The effectiveness of these models is confirmed by confusion matrix and ROC curve analyses, with the combined models showing particularly high accuracy in assessing susceptibility. High risk zones were identified in specific areas and along major fault zones in Liangshan Prefecture. The research provides significant insights into the susceptibility of geological hazards in mountainous and canyon regions, offering a comprehensive approach that goes beyond the limitations of single model applications. This methodology not only provides more accurate and comprehensive results, but also serves as a fundamental reference for geological hazard mitigation and management in Liangshan Prefecture, potentially benefiting similar regions worldwide. [ABSTRACT FROM AUTHOR]

Rapid and accurate landslide mapping following extreme triggering events is critical for emergency response, hazard prevention, and disaster management. Artificial intelligence- based approaches enable rapid landslide mapping, yet the lack of a high-resolution globally distributed and event-based dataset poses a severe challenge in developing generalized machine learning models for landslide detection. This paper addresses this issue by designing a diverse coseismic landslide dataset, the Globally Distributed Coseismic Landslide Dataset (GDCLD), which includes multi-source remote sensing images (i.e., PlanetScope, Gaofen-6, Map World, and Unmanned Aerial Vehicle) encompassing various geographical and geological backgrounds worldwide. The GDCLD can be accessed through this link: https://doi.org/10.5281/zenodo.11369484 (Fang et al., 2024). Furthermore, we evaluate the potential of GDCLD by analyzing mapping performance of the seven most popular semantic segmentation algorithms. We further validate the generalization capabilities of the dataset by deploying the models on three types of remote sensing images from four independent regions. Besides, we also assess the model on rainfall-induced landslide dataset and achieve good results, demonstrating its applicability in landslide segmentation under other triggering factors. The results indicate the superiority of the proposed dataset in landslide detection, offering a robust mapping solution for rapid assessment in future extreme events that trigger landslides across the globe. [ABSTRACT FROM AUTHOR]

This study aims to assess the musculoskeletal risk of military personnel on a Leopard 2 A6 main battle tank crew and to identify associated factors for future prevention and mitigation strategies. A sample of 57 Portuguese military personnel, who are or were part of the Leopard 2 A6 main battle tank crew, answered a questionnaire on their perception of task performance, considering muscle demands, comfort, posture, movements, and associated symptoms. A subsample of four soldiers from the Armoured Squadron of the Portuguese Mechanized Brigade were assessed using an inertial measurement unit system and underwent a whole-body kinematic analysis coupled with a Rapid Entire Body Assessment during a simulated two-hour mission. The results indicate that soldiers accurately perceive their roles within the crew and that, overall, there is a high risk of musculoskeletal injuries in all tasks. However, tasks directly related to the crew's primary duties carry consistently high risk when considering the time spent on their tasks. This study highlights the need for targeted preventive measures to reduce the incidence and severity of injuries among the crew of the Leopard 2 A6 main battle tank. [ABSTRACT FROM AUTHOR]

The museum system is exposed to a high risk of seismic hazards. However, it is difficult to carry out seismic hazard prevention to protect cultural relics in collections due to the lack of real data and diverse types of seismic hazards. To address this problem, we developed a deep-learning-based multi-source feature-fusion method to assess the data on seismic damage caused by collected cultural relics. Firstly, a multi-source data-processing strategy was developed according to the needs of seismic impact analysis of the cultural relics in the collection, and a seismic event-ontology model of cultural relics was constructed. Additionally, a seismic damage data-classification acquisition method and empirical calculation model were designed. Secondly, we proposed a deep learning-based multi-source feature-fusion matching method for cultural relics. By constructing a damage state assessment model of cultural relics using superpixel map convolutional fusion and an automatic data-matching model, the quality and processing efficiency of seismic damage data of the cultural relics in the collection were improved. Finally, we formed a dataset oriented to the seismic damage risk analysis of the cultural relics in the collection. The experimental results show that the accuracy of this method reaches 93.6%, and the accuracy of cultural relics label matching is as high as 82.6% compared with many kinds of earthquake damage state assessment models. This method can provide more accurate and efficient data support, along with a scientific basis for subsequent research on the impact analysis of seismic damage to cultural relics in collections. [ABSTRACT FROM AUTHOR]

Characterization of debris flow is critical to both risk assessment and hazard mitigation. Recent technologies enable onsite environmental monitoring sensors for geological disaster monitoring. However, the spatiotemporal understanding of debris flow in remote mountainous areas is still limited due to difficulties in observation networks and its complex driving conditions. Here we apply a coupled surface–subsurface hydrological model to examine the characteristics of the water movements near debris flow sites in southwest China. Our approach captured the temporal dynamics of infiltration, redistribution of soil moisture, groundwater storage, and lateral groundwater fluxes. The lateral groundwater flux and groundwater storage were informative indicators in identifying debris flow location. Such informativeness was only effective when hourly dynamics were analysed. Our findings provide new insight into quantifying debris flow susceptibility. This study suggests that the coupled surface–subsurface watershed modelling approach can be informative for preliminary monitoring, planning and management of debris flow. [ABSTRACT FROM AUTHOR]

Eastern North Carolina (ENC), a predominantly rural region, experiences the intersection of fluvial, pluvial, and tidal flooding, which leads to complex and impactful outcomes. Managing these, often compounding, hazards is challenging, especially as climate change drivers, such as sea-level rise and more intense storms will likely lead to a greater incidence of compound coastal water events (CCWE). Using data from focus group interviews conducted with 41 planners and emergency managers, we examine the mitigation approaches pursued by rural counties in ENC located along the coast and those adjacent to it that share estuarine environments or linked riverine systems. Findings show that communities seek funding to clear streams and channels, to implement buyouts and elevation projects and for infrastructure maintenance. Communities also undertake floodplain management to minimize flood exposure while facing several barriers to adequate mitigation. [ABSTRACT FROM AUTHOR]

During 2019 to 2022, a comprehensive study was conducted across 71 stations spanning over 15 states in India to investigate air quality variations during the pre-Diwali, Diwali, and post-Diwali periods. The investigation, centred on the extensive use of firecrackers during festive times, revealed a short-term deterioration in air quality with long-lasting impacts. PM2.5 and PM10 levels were found to significantly increase during Diwali and the post-Diwali period compared to the pre-Diwali period. The levels of PM2.5 and PM10 experience respective increases of 44.66% and 11.03%, 29.12% and 05.41%, 52.63% and 23.38%, and 39.65% and 17.61% during Diwali period from 2019 to 2022, where 68.84% and 34.32%, 44.42% and 29.42%, and 48.70% and 25.22% in 2019, 2021 and 2022 in post-Diwali period compared to the pre-Diwali. The Mann–Kendall and Sen's slope analyses reveal statistically significant positive trends (increasing) in both PM10 and PM2.5 levels during the Diwali festival period. Notably, the study introduced the Health Hazard-based Air Quality Index (HAQI), which demonstrated higher values than the National Ambient Air Quality Index (NAAQI). Unlike NAAQI, which considers individual pollutant sub-index values, and considered highest one of sub-index values in AQI analysis. HAQI provides a more comprehensive assessment of health risks associated with exposure to multiple pollutants effects. In 2022, AQI and HAQI values during pre-Diwali were 114 and 146.18, during Diwali were 173 and 332.18, and post-Diwali were 191 and 414.14, respectively. These findings underscore the elevated risk during Diwali and the post-Diwali period due to heightened pollution levels, emphasizing the need for public awareness and potential mitigation strategies. The study calls attention to the importance of considering cumulative health impacts and suggests the adoption of HAQI for more informative air quality assessments. [ABSTRACT FROM AUTHOR]

The utilization of baffle structures as a highly effective strategy for mitigating debris flow has attracted significant scholarly attention in recent years. Although the predominant focus of existing research has been on augmenting the energy dissipation capabilities of baffle structures, their deformation behavior under impact load has not been extensively investigated. Addressing this research gap, the current study systematically designs a series of physical model experiments, incorporating variables such as baffle height, shape, and various combinations of baffle types to comprehensively analyze the deformation characteristics of baffles subjected to debris flow impact. The experimental results reveal that the deformation of baffle group structures demonstrates a marked non-uniform spatial distribution and exhibits a latency effect. Additionally, distinct baffle configurations show considerable variations in peak strain, suggesting that combining different baffle shapes can not only optimize energy dissipation but also enhance resistance to deformation. Moreover, the relationship between baffle height and the development of deformation in relation to energy dissipation capacity is inconsistent, indicating that deformation must be a key consideration in the design of baffle structures. Consequently, this paper advocates for the formulation of a deformation-based design strategy for baffle structures, with the findings presented herein providing a foundational reference for future studies. [ABSTRACT FROM AUTHOR]

The threat and destructiveness of landslide disasters caused by extreme rainfall are increasing. Rainfall intensity is a key factor in the mechanism of rainfall-induced landslides. However, under natural conditions, rainfall intensity is highly variable. This study focuses on the Fanling landslide and investigates the effects of varying rainfall intensity amplitudes, rainfall durations, and total rainfall amounts on landslide behavior. Three experimental groups were established, and ten rainfall conditions were simulated numerically to analyze the seepage field response of the landslide under fluctuating rainfall conditions. The results indicate that (1) there are positive correlations between the final pore pressure and both the amplitude and duration of rainfall intensity; (2) the pore water pressure response in the upper slope changes significantly, initiating deformation; and (3) the total rainfall amount is the most direct factor affecting the pore pressure response and landslide deformation. Compared to long-term stable rainfall, short-term fluctuating rainstorms are more likely to trigger landslides. These findings enhance our understanding of landslide mechanisms under fluctuating rainfall, providing valuable insights for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

With the development of earthquake disaster reduction efforts in China, the content of earthquake disaster reduction policies has become increasingly enriched and improved. Particularly, multiple provincial governments have proposed earthquake disaster reduction planning policies. It is important to explore whether these policies can affect disaster mitigation. Therefore, this paper summarizes the earthquake disaster reduction plans and factors influencing seismic resilience. Panel data from 24 provinces between 2012 and 2021 were collected, and a difference-in-differences approach was used to construct an econometric model to evaluate the policy effects and analyze the enhancement of seismic resilience. The results show that the implementation of earthquake disaster reduction policies has a positive impact on earthquake monitoring, evacuation, and emergency relief capabilities, and the estimated policy effects are statistically significant. Moreover, a series of tests were conducted. The conclusions are as follows: (1) Earthquake disaster reduction policies have a positive impact on the improvement of seismic resilience in provinces. (2) Provinces with a higher number of earthquakes experience more significant effects from earthquake disaster reduction policies. (3) Provinces with higher seismic peak ground acceleration values exhibit more pronounced improvements in seismic resilience. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate circadian rhythms in independently living adults with obesity and mental disease, exploring the interplay between biological markers and lifestyle factors. Eighty participants divided equally into four groups; (i) people with obesity and schizophrenia; (ii) people with obesity and bipolar disorder; (iii) people with obesity without mental disease or sleep disorders, and (iv) people without obesity, mental disease or sleep disorders. Over two consecutive days, participants engage in repeated self-sampling of hair follicle and saliva; concurrently, data is collected on diet, body temperature, light exposure, sleep parameters, and physical activity by accelerometry. Hair follicles are analyzed for circadian gene expression, saliva samples for cortisol and melatonin concentrations. Circadian rhythms are investigated by cosinor analysis. The study employs a participant-tailored sampling schedule to minimize disruptions to daily routine and enhance ecological validity. The methodology aims to provide a comprehensive insight into the factors contributing to circadian disruptions in people with obesity, bipolar disorder and schizophrenia, potentially informing strategies for future management and mitigation. Trial registration: (ClinicalTrials.gov Identifier: NCT05413486). [ABSTRACT FROM AUTHOR]

The Northwestern Yunnan Region (NWYR), located on the southeastern edge of the Tibetan Plateau, is characterized by a combination of low-crustal flow and gravitational collapse, giving rise to a complex network of active faults. This presents significant seismic hazards, particularly due to the potential for multi-segment ruptures and resulting landslides, as demonstrated by the historical 1515 M 7.8 Yongshen Earthquake. This article presented a novel seismic hazard modeling study for the NWYR, integrating fault slip parameters and assessing multi-segment rupturing risks. Among the four potential multi-segment rupture combination models examined, Model 1, characterized by multi-segment rupture combinations on single faults, particularly fracturing the Zhongdian fault, emerges as the most suitable for the NWYR, supported by the alignment of modeled seismicity rates with fault slip rates. Our analysis demonstrated that peak ground-motion acceleration values, calculated with a 475-year return period from modeled seismicity rates, exhibited a strong correlation with fault distribution, averagely higher than the China Seismic Ground Motion Parameters Zonation Map. Furthermore, we conducted simulations to forecast landslide occurrence probabilities across our peak ground-motion acceleration distribution map. Our findings underscored that the observed combinations of multi-segment ruptures and their associated behaviors were in alignment with the small block rotation triggered by the gravitational collapse of the Tibetan Plateau. This result highlighted the intricate interplay between multi-segment rupturing hazards and regional geological dynamics. [ABSTRACT FROM AUTHOR]

The external dynamic conditions, such as reservoir capacity and inflow rate, significantly impact the overtopping breach process of the landslide dam. This paper systematically investigates changes in erosion characteristics at different stages of the overtopping breach process under various reservoir capacities and inflow rates through flume experiments, with the aim of revealing the response mechanisms of breach duration and peak discharge to external dynamic conditions. The research results indicate that reservoir capacity and inflow rate significantly influence backward erosion efficiency by controlling the rate of water level change during the initial breaching stage of the landslide dam, which affects breach duration. Reservoir capacity and inflow rate affect the peak discharge by controlling the lateral erosion rate of the breach, thus influencing the lateral widening efficiency during the accelerated erosion stage. There is an inconsistent response between vertical and lateral erosion to changes in external dynamic conditions during the accelerated erosion stage, with vertical erosion being significantly more suppressed. These findings contribute to a deeper understanding of the overtopping breach mechanism of landslide dams and provide valuable insights for flood control and disaster mitigation efforts. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) is an important tool in probabilistic seismic risk analysis (PSRA) due to its holistic nature and powerful probabilistic inference capabilities. However, while the information that can be stored by BN includes variable values, probability distributions, and relationships between variables, it does not involve event quantities. The results obtained from PSRA and probabilistic seismic hazard analysis (PSHA) are commonly frequency‐related (the number of occurrences per unit time), fundamentally describing event numbers. BN assumes a fixed event number relationship between nodes, but the aftershock counts vary for different mainshock magnitudes. This limitation further restricts its effectiveness in lifespan PSRA for structures. Therefore, this study aims to propose an enhanced BN (EBN) for modeling aftershock PSHA (APSHA) considering frequency information. This paper first introduces the modeling method using BN, including PSHA and a simplified model of APSHA using Båth's law. Next, an EBN that can effectively address frequency issues between the mainshock and aftershocks is presented for modeling APSHA considering the modified Omori's law, while the mathematical principles of both forward and backward inferences for the EBN are elaborated in detail. Then, the article carefully analyzes the utilization of the EBN for APSHA modeling under different scenarios where the information regarding the mainshock is known to varying extents, followed by the extension of the EBN in a time domain to obtain time‐dependent aftershock hazard. Finally, an example of APSHA in a specific location in China is illustrated. [ABSTRACT FROM AUTHOR]

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

Knowledge of hazard conditions due to mass movements is one of the non-structural measures for risk management, urban planning, and protection of natural resources. To obtain this type of mapping, a spatial construction was started by correlating the historical movements with the inherent variables of the terrain by means of the bivariate statistical method, which assigns densities or weights of evidence to estimate the degree of susceptibility. This model was combined with the triggering factors (rainfall and earthquake) to determine the spatiotemporal conditions (hazard). From this procedure, it was obtained that the susceptibility model presents 34% (32.33 km2) of the total area in the high and very high categories, especially in the regions of Mount Siradella and Mount Faro. The validation of the present model obtained a value of 0.945 with the ROC curve. For the hazard condition, 34.1% (32.06 km2) of the study area was found to be in the high and very high category, especially in the municipalities of El Grove, Sanxenxo, and A Illa de Arousa, which have the greatest extension. The present evaluation is an advance in the knowledge of the risk and the actions that can be derived, as in turn, this type of study is an easy tool to obtain due to its low cost and information processing. [ABSTRACT FROM AUTHOR]