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The Maltese archipelago is renowned for its spectacular coasts, characterized by vertical cliffs and scree slopes. In the western sector of Malta and the eastern region of Gozo, a marly clay formation with ductile properties underlying a stiff limestone unit has led to relevant lateral spreading. Utilizing drone aerial photogrammetry, digital elevation models, and satellite imagery, we analyzed the ongoing geomorphological processes across five promontories, selected as case studies. Our analysis reveals a complex interaction between geological structures, Quaternary sea level fluctuations, and lateral spreading processes. Photogrammetric models show that once detached, blocks from the plateaus tend to topple and fall or experience subsidence and backtilting. At Rdum il-Qammieħ, fractures up to 250 m long and openings of up to 2 m were observed, while at Sopu, detached blocks exhibit subsidence of up to 50% and rotations nearing 60°. In all the studied promontories, rotational slides predominantly occur at the frontal sectors, while toppling mechanisms are more common along scarp-edged plateaus. The thickness ratio between the stiff and the ductile formation, ranging from 0.13 to 1.12, along with slope gradients between 10° and 41°, further influence the stability of these coastal features. We discuss the structural and sea level influences on Maltese coastal cliff development over the last 125 ky. We propose a conceptual model outlining the evolution of the Malta Graben promontories through a three-stage evolutionary model: proto-promontories, cliff demolition, and isolation. This model emphasizes the significant role of predisposing, preparatory, and triggering factors in the geomorphological evolution of the Maltese coastline. Our findings provide essential insights into the landscape changes in the Maltese archipelago and represent a useful tool for coastal management and hazard mitigation strategies. [ABSTRACT FROM AUTHOR]

Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) The equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB, with a standard deviation of 8.52 dB, which reveals a high noise intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train's mechanical systems, wheel–rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit's operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers' hearing health, enhancing work efficiency, and ensuring driving safety. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The exploitation of clean and efficient fire extinguishing materials has substantial implications for improving disaster prevention, mitigation, and relief capabilities, maintaining public safety, and protecting people's lives and property as well as the natural environment. Natural polymer hydrogel with high water containment, excellent film formation, high heat insulation, ecofriendliness, and degradability has huge potential in achieving new breakthroughs for developing clean and efficient fire extinguishing materials and products. In recent years, the exploitation of hydrogel extinguishing materials and the fabrication of products has attracted great attention, gradually replacing traditional fire extinguishing products. In this perspective, an in-depth review of the evolution of hydrogels applied for fire extinguishing and prevention is presented. Firstly, the extinguishing principles of hydrogel extinguishants are explained. Secondly, the preparation strategies and evaluation system of the hydrogel extinguishants are emphatically discussed. Although great progress has been made in developing high-performance hydrogel extinguishants, it remains challenging to develop cost-effective, degradable, and easy-to-use hydrogel extinguishants. Additionally, we highlight the importance of considering the commercial aspects of hydrogel extinguishants. Looking into the future, hydrogel extinguishants are promising, but continued investment in research and development is necessary to overcome the challenges. [ABSTRACT FROM AUTHOR]

After a landslide, swift and precise identification of the affected area is paramount for facilitating urgent rescue operations and damage assessments. This is particularly vital for land use planners and policymakers, enabling them to efficiently address hazard mitigation, the resettlement of those affected by the hazards, and to strategize land planning in the impacted regions. Despite the importance, conventional methods of monitoring landslides often fall short due to their restricted scope and the challenges associated with data acquisition. This study proposes a landslide detection method based on unsupervised multisource and target domain adaptive image segmentation (LUDAS) that is capable of achieving robust and generalized landslide mapping across multiple sources and target domains. Specifically, LUDAS consists of two phases. In the first phase, we introduce an unsupervised interdomain translation network to align the styles of multiple source domains to multiple target domains, generating pseudotarget domain data. Our interdomain translation network is capable of style transfer between any two domains. Through careful design of the network structure and loss functions, we ensure effective style transfer while preserving the content structure of the source domain images. In the second phase, the landslide segmentation model is trained in a supervised manner using annotated data from multiple source domains and multiple pseudotarget domains, resulting in a model with strong generalization capabilities that can adapt to multiple source and target domains. Finally, through extensive qualitative and quantitative analysis experiments, our study confirms that the proposed domain-adaptive segmentation model not only achieves exceptional landslide segmentation performance across multiple target domains but also, due to its good generalizability and transferability, has great potential for application in the emergency response to landslide. This capability can provide strong support for post-disaster emergency rescue, disaster assessment, and land planning in areas with scarce data. [ABSTRACT FROM AUTHOR]

This study employs advanced geospatial analytical techniques to evaluate the vulnerability of Nigeria's coastal states and their constituent local government areas to flood hazards, which represent a critical and escalating risk within the coastal hazard paradigm intensified by climate change phenomena. The study's objective is to utilize geospatial data to delineate and quantify the intensity and distribution of flood susceptibility, thus establishing a foundational framework for developing comprehensive disaster management strategies in response to the challenges posed by climate variability. The research uses satellite imagery and geographic information system (GIS)-based hydrological modeling to delineate regions susceptible to flooding, synthesizing topographical and hydrological data to stratify areas into discrete flood susceptibility categories. The findings indicate that the Delta coastal State of Nigeria contains extensive medium to high-risk flood zones spanning 8304.57 km2. While the Bayelsa coastal State of Nigeria presents critical areas at high to very high flood risk, encompassing 5506.61 km2 at high risk and 1826.88 km2 at very high risk, this highlights the urgent necessity for immediate and strategic mitigation measures. This research highlights the critical importance of geospatial technology in shaping disaster management and enhancing community resilience against increasing flood frequencies. As Nigeria's coastal regions face escalating flood susceptibility, advanced geospatial methods are vital for assessing and mitigating these climate-induced threats, contributing to climate-resilient planning and aligning with Sustainable Development Goal 13: Climate Action. The study's geospatial approach delivers precise flood risk evaluations and guides targeted mitigation efforts, marking significant progress in managing coastal hazards in a changing climate. [ABSTRACT FROM AUTHOR]

The western region of Sichuan Province, located in the Hengduan Mountains, is one of China's most seismically active zones. Due to limited land resources, many villages in western Sichuan Province are situated in mountainous and valley areas, facing harsh climatic conditions, lagging economic development, and insufficient infrastructure. After experiencing seismic disasters, these villages often encounter challenges, such as cut-off mountain roads, difficulties in delivering relief supplies, and sharply a significant drop in nighttime temperatures due to the high-altitude climate. Consequently, in the case of the economic underdevelopment and limited resources, how to repurpose existing buildings for disaster relief has become a crucial issue for disaster prevention and mitigation in these villages. This paper takes Ganbao Tibetan Village, located in the alpine gorge regions of western Sichuan Province, as a case study. It evaluates the interior space utilization status of different types of existing public buildings during disasters, calculates the effective utilization rate of interior space disaster reduction conversion based on the spatial layout model, and extracts the typical interior space dimensions that are conducive to disaster relief and conversion of public buildings based on the interior space utilization characteristics of disaster relief. This study provides a reference for the design of public service buildings considering the requirements of disaster resilience transformation. [ABSTRACT FROM AUTHOR]

The Baihetan Hydropower Station reservoir area began impoundment in 2021, triggering the reactivation of ancient landslides and the formation of new ones. This not only caused direct landslide disasters but also significantly increased the likelihood of secondary surge wave disasters. This study takes the Wangjiashan (WJS) landslide in the Baihetan reservoir area as an example and conducts large-scale three-dimensional physical model experiments. Based on the results of the physical model experiments, numerical simulation is used as a comparative verification tool. The results show that the numerical simulation method effectively reproduces the formation and propagation process of the WJS landslide-induced surge waves observed in the physical experiments. At the impoundment water level of 825 m, the surge waves generated by the WJS landslide pose potential threats to the Xiangbiling (XBL) residential area. In this study, the numerical simulation based on computational fluid dynamics confirmed the actual propagation forms of the surge waves, aligning well with the results of the physical experiments at a microscopic scale. However, at a macroscopic scale, there is some discrepancy between the numerical simulation results and the physical experiment outcomes, with a maximum error of 25%, primarily stemming from the three-dimensional numerical source model. This study emphasizes the critical role of physical model experiments in understanding and mitigating surge wave disasters in China. Furthermore, physical experiments remain crucial for accurate disaster prediction and mitigation strategies. The theories and methods used in this study will provide important references for future research related to landslide disasters in reservoir areas. [ABSTRACT FROM AUTHOR]

High-energy events such as tsunamis pose significant threats to coastal infrastructure and buildings, necessitating effective mitigation strategies to minimize damage. Compared to massive construction measures, buffer blocks are increasingly recognized as a potential solution for reducing the impact of such events. Understanding their effectiveness and optimizing their placement configurations is crucial for enhancing coastal resilience. The present study aims to experimentally evaluate the influence of buffer blocks on the depth of tsunami inundation. By comparing different configurations of buffer blocks, this study seeks to identify the most effective one for reducing the inundating flow depth. The laboratory tests revealed that the buffer blocks significantly influence flow depth and its characteristics. For ratios of flow depth to block height (R) below 1.5, the buffer blocks exhibited a delay in the arrival of the flow and effectively reduced the flow depths. However, for R values greater than 1.5, the effectiveness reduced as the flow began to overtop the obstructions. The increase in the flow depth at the buffer blocks was the least for a single-row configuration, while the multiple-row configurations with wider spacing offered higher flow resistance and increased the flow depths in front of the blocks. These findings suggest that strategically configured buffer blocks can substantially enhance coastal protection against high-energy flow events during natural coastal hazards. This study provides critical insights into the design and optimization of buffer block configurations, contributing to improved coastal infrastructure resilience and disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

The ability to accurately map tropical wetland dynamics can significantly contribute to a number of areas, including food and water security, protection and enhancement of ecosystems, flood hazard management, and our understanding of natural greenhouse gas emissions. Yet currently, there is not a tractable solution for mapping tropical forested wetlands at high spatial and temporal resolutions at a regional scale. This means that we lack accurate and up-to-date information about some of the world's most significant wetlands, including the Amazon Basin. RadWet-L is an automated machine-learning classification technique for the mapping of both inundated forests and open water using ALOS ScanSAR data. We applied and validated RadWet-L for the Amazon Basin. The proposed method is computationally light and transferable across the range of landscape types in the Amazon Basin allowing, for the first time, regional inundation maps to be produced every 42 days at 50 m resolution over the period 2019–2023. Time series estimates of inundation extent from RadWet-L were significantly correlated with NASA-GFZ GRACE-FO water thickness (Pearson's r = 0.96, p < 0.01), USDA G-REALM lake hight (Pearson's r between 0.63 and 0.91, p < 0.01), and in situ river stage measurements (Pearson's r between 0.78 and 0.94, p < 0.01). Additionally, we conducted an evaluation of 11,162 points against the input ScanSAR data revealing spatial and temporal consistency in the approach (F1 score = 0.97). Serial classifications of ALOS-2 PALSAR-2 ScanSAR data by RadWet-L can provide unique insights into the spatio-temporal inundation dynamics within the Amazon Basin. Understanding these dynamics can inform policy in the sustainable use of these wetlands, as well as the impacts of inundation dynamics on biodiversity and greenhouse gas budgets. [ABSTRACT FROM AUTHOR]

Understanding the distribution and development patterns of mining-induced fissures is crucial for environmental protection and geological hazard prevention. To address labor-intensive manual inspection, an automated approach leveraging Convolutional Neural Networks (CNNs) and Unmanned Aerial System Photogrammetry (UASP) is proposed for fissure identification and mapping. Initially, the ResNet-50 network was employed for the binary classification of the cropped UASP orthophoto images. A comparative analysis was conducted to determine the optimal model between DeepLabv3+ and U-Net. Subsequently, the identified fissures were mosaicked and spatially projected onto the original orthophoto image, incorporating precise projection data, thereby furnishing a spatial reference for environmental governance. The results indicate a classification accuracy of 93% for the ResNet-50 model, with the U-Net model demonstrating a superior identification performance. Fissure orientation and distribution patterns are influenced by the mining direction, ground position of the mining workface, and topographic undulations. Enhancing the CNN performance can be achieved by incorporating variables such as slope indices, vegetation density, and mining workface locations. Lastly, a remote unmanned approach is proposed for the automated mapping of mining-induced fissures, integrated with UAS automated charging station technology. This study contributes to the advancement of intelligent, labor-saving, and unmanned management approaches advocated by the mining industry, with potential for broad applications in mining environmental protection efforts. [ABSTRACT FROM AUTHOR]

Geological landslide disasters significantly threaten the safety of people's lives and property. Landslides are a significant threat in Zayu County, Tibet, resulting in numerous geological disasters, including the 1950 earthquake that caused significant casualties and river blockages. More recent landslides have caused substantial economic losses and infrastructure damage, posing ongoing risks to the local population and their property. Landslide hazard assessment is a critical task in geological disaster prevention and mitigation. This study applied the Inception model to assess landslide hazard in the Zayu area. The Inception model excels at capturing multi-scale features efficiently through its architecture. Fifteen disaster-causing factors were selected as the primary indicators for landslide susceptibility assessment. On this basis, the Inception model was used for landslide susceptibility assessment. Combined with daily precipitation data in the Zayu area, the landslide hazard assessment of the "25 April 2010, heavy rainstorm in Zayu, Tibet" was completed. Back Propagation Neural Network (BPNN), Residual Neural Network (ResNet), Convolutional Neural Network (CNN), and Visual Geometry Group-16 (VGG-16) were introduced for comparison of the fitting effects, and SHapley Additive exPlanations (SHAP) was used for interpretability analysis. The comparative experimental results show that the Inception model performed best in landslide susceptibility assessment and is feasible in practical use. The results also show that the most critical factors in the model were topographic wetness index (TWI), normalized difference water index (NDWI), and road density. This study is significant for assessing landslide hazard in geological landslide disaster prevention and mitigation. It provides a reference for further research and response to similar disasters. [ABSTRACT FROM AUTHOR]

Rock bursts are dynamic phenomena in underground openings, causing damage to support and infrastructure, and are one of the main natural hazards in underground coal mines. The prediction of rock bursts is important for improving safety in mine openings. The hazard of rock bursts is correlated with seismic activity, but rock bursts are rare compared to non-destructive tremors. The five machine learning classifiers (multilayer perceptron, adaptive boosting, gradient boosting, K-nearest neighbors, and Gaussian naïve Bayes), along with an ensemble hard-voting classifier composed of these classifiers, were used to recognize rock bursts among the dominant non-destructive tremors. Machine learning models were trained and tested on ten sets of randomly selected data obtained from one of the active hard coal mines in the Upper Silesian Coal Basin, Poland. For each of the 627 cases in the database, 15 features representing geological, geomechanical, mining, and technical conditions in the opening as well as tremor energy and correlated peak particle velocity were determined. Geological and geomechanical parameters of the coal seams and surrounding rocks were aggregated into a single GEO index. The share of rock bursts in the database was only about 8.5%; therefore, the ADASYN balancing method, which addresses imbalanced datasets, was used. The ensemble hard-voting classifier most effectively classified rock bursts, with an average recall of 0.74. [ABSTRACT FROM AUTHOR]

Geological disasters in large alpine reservoirs primarily take the form of landslide occurrences and are predominantly induced by slope instability. Presently, risk monitoring and assessment strategies tend to prioritize sudden alerts overlooking progressive trajectories from the onset of creeping deformations within the slope to its critical state preceding landslides. Hence, analyzing landslide safety risks over time demonstrates a significant degree of hysteresis, highlighting the necessity for a comprehensive approach to risk assessment that encompasses both gradual and sudden precursors to landslide events. This study analyzes the factors affecting slope stability and establishes a slope evaluation indicator system that includes terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestation. It proposes a quantitative model for slope landslide risk assessment based on a fuzzy broad learning system, aiming to accurately assess slopes with different risk levels. The overall assessment accuracy rate reaches 92.08%. This multi-dimensional risk assessment model provides long-term monitoring of slope conditions and scientific guidance on landslide risk management and disaster prevention and mitigation on a long time scale for risky slopes in reservoir areas. [ABSTRACT FROM AUTHOR]

As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang—including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou—exhibiting the highest levels of risk. [ABSTRACT FROM AUTHOR]

Extreme precipitation of a minimum daily value of >30 mm often initiates natural hazards such as floods, which in turn may not only lead to property damage but also present a danger to people's health and lives. This paper mainly focuses on examining the trends and frequency of extreme daily precipitation (EDPr) in Poland. Also, it determines natural risk zones caused by EDPr of >30 mm, >50 mm, >70 mm, and >100 mm. In Poland, a significant positive trend was found for EDPr > 30 mm, >50 mm, and >70 mm in September, and for EDPr >100 mm in May. The most frequently recorded EDPr in Poland was >30 mm, the frequency of which ranged from 0.04% in February to nearly 3% in July. EDPr of >100 mm was recorded in 4 months, from May to August. An increase in the frequency of monthly EDPr in Poland occurred mainly in the southwestern and western parts. In Poland, three hazard zones of various frequencies of EDPr events were determined. In Zone III, which is in the southwestern and southern parts of the country, EDPr events occurred far more often than in Zone I; on average, four times more in the spring–summer season and slightly more than five times more in the autumn–winter season. The obtained results may help in the building of modern management and monitoring systems for the prevention of natural hazards caused by extreme precipitation. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides are a major hazard in the Three Gorges Reservoir area (TGRA) of China, encompassing 19 districts and counties with extensive coverage and significant spatial variation in terrain. This study introduces the Gradient Boosting Decision Tree (GBDT) model, implemented on the Google Earth Engine (GEE) cloud platform, to dynamically assess landslide risks within the TGRA. Utilizing the GBDT model for landslide susceptibility analysis, the results show high accuracy with a prediction precision of 86.2% and a recall rate of 95.7%. Furthermore, leveraging GEE's powerful computational capabilities and real-time updated rainfall data, we dynamically mapped landslide hazards across the TGRA. The integration of the GBDT with GEE enabled near-real-time processing of remote sensing and meteorological radar data from the significant "8–31" 2014 rainstorm event, achieving dynamic and accurate hazard assessments. This study provides a scalable solution applicable globally to similar regions, making a significant contribution to the field of geohazard analysis by improving real-time landslide hazard assessment and mitigation strategies. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) can be devastating events for vulnerable countries like Vanuatu, impacting their population, livelihoods, and infrastructure, leaving the country in need of aid and recovery. Despite this, comprehensive risk information on the nuanced impacts of each region is not well understood. Every TC event is different, and understanding the potential for impact at each location empowers decision makers in the lead-up to an event or during off-season planning to make more informed decisions to direct disaster risk reduction efforts. TC hazard model data typically describe intensity and likelihood, which can be fed into risk assessment frameworks to describe probabilistic risk. This study instead uses freely available remote sensing data to create proxies for the TC hazards of storm surge and flooding and to describe only the intensity of the hazard if the event occurs at the location. This hazard susceptibility index is fed into a risk assessment framework with Vanuatu exposure and vulnerability data for domains of populations, housing, and roads. These methods allow for the risk to be estimated for each month, as well as during specific historical time periods of TC Pam, TC Harold, and the TCs Judy and Kevin, enabling future impact validation. The results show households to have the highest risk, followed by roads and population domains, while a TC-induced surge risk is overall higher than TC-induced flooding, particularly in the road domain. The results, however, show a likely underestimation of event hazards and an overestimation of Port Vila's resistance to impacts, which is a subject of future investigation and validation. [ABSTRACT FROM AUTHOR]

Simple Summary: Larvae of the black soldier fly can eat organic waste and grow into nutritious sources of protein and fat for animal feed. However, if the waste they eat is contaminated with toxins or dangerous microbes, a risk exists that the larvae will become contaminated as well. This review examines the research on ways to decontaminate waste before feeding it to larvae or decontaminate larvae after feeding them waste to identify the safest way to minimize the risk to consumers from such hazards. Sample treatments include using heat or disinfectants; however, these can affect larval growth rates or the quality of the final product. Using less contaminated wastes may be required for black soldier fly larvae destined for use as animal feed, whereas larvae fed highly contaminated wastes can be directed to non-feed uses. The black soldier fly larva, Hermetia illucens, can efficiently convert organic waste into biomatter for use in animal feed. This circularity comes with a risk of contaminating downstream consumers of the larval products with microbes, heavy metals, and other hazards potentially present in the initial substrate. This review examines research on mitigation techniques to manage these contaminants, from pretreatment of the substrate to post-treatment of the larvae. While much research has been done on such techniques, little of it focused on their effects on food safety contaminants. Cheap and low-technology heat treatment can reduce substrate and larval microbial load. Emptying the larval gut through starvation is understudied but promising. Black soldier fly larvae accumulate certain heavy metals like cadmium, and their ability to process certain hazards is unknown, which is why some government authorities are erring on the side of caution regarding how larval bioconversion can be used within feed production. Different substrates have different risks and some mitigation strategies may affect larval rearing performance and the final products negatively, so different producers will need to choose the right strategy for their system to balance cost-effectiveness with sustainability and safety. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach to landslide detection by incorporating the Spatial and Band Refinement Convolution (SBConv) module into the U-Net architecture, to extract features more efficiently. The original U-Net architecture employs convolutional layers for feature extraction, during which it may capture some redundant or less relevant features. Although this approach aids in building rich feature representations, it can also lead to an increased consumption of computational resources. To tackle this challenge, we propose the SBConv module, an efficient convolutional unit designed to reduce redundant computing and enhance representative feature learning. SBConv consists of two key components: the Spatial Refined Unit (SRU) and the Band Refined Unit (BRU). The SRU adopts a separate-and-reconstruct approach to mitigate spatial redundancy, while the BRU employs a split-transform-and-fuse strategy to decrease band redundancy. Empirical evaluation reveals that models equipped with SBConv not only show a reduction in redundant features but also achieve significant improvements in performance metrics. Notably, SBConv-embedded models demonstrate a marked increase in Recall and F1 Score, outperforming the standard U-Net model. For instance, the SBConvU-Net variant achieves a Recall of 75.74% and an F1 Score of 73.89%, while the SBConvResU-Net records a Recall of 70.98% and an F1 Score of 73.78%, compared to the standard U-Net's Recall of 60.59% and F1 Score of 70.91%, and the ResU-Net's Recall of 54.75% and F1 Score of 66.86%. These enhancements in detection accuracy underscore the efficacy of the SBConv module in refining the capabilities of U-Net architectures for landslide detection of multisource remote sensing data. This research contributes to the field of landslide detection based on remote sensing technology, providing a more effective and efficient solution. It highlights the potential of the improved U-Net architecture in environmental monitoring and also provides assistance in disaster prevention and mitigation efforts. [ABSTRACT FROM AUTHOR]

The formation and evolution of management gullies is a highly intense process of soil erosion often overlooked in policies and river basin strategies. Despite the worldwide spread of the phenomenon, our ability to assess and simulate gullying and its impacts remains limited; therefore, predicting the development and evolution of these river reaches represents a significant challenge, especially in areas where the loss of productive soil or the hazards linked to landslides or floods represent critical factors. Our study demonstrates how an exclusively morphometric approach, based on the construction of the hypsometric curve and applied to small hydrographic basins that are lithologically homogeneous and hierarchized according to the Strahler classification method, is able to predict the triggering height of the gullies; this height corresponds to the mean elevation of the basin and the inflection point of the hypsometric curve itself, confirming the hypothesis that this point coincides with the point at which a sudden change in surface runoff energy occurs, The study also shows that the portion of the basin necessary to trigger these intense erosive processes is always within a small range, regardless of the size and morphology (slope) of the basin itself. Such an approach, which is quick and relatively easy to apply, could help develop hydrogeological hazard mitigation practices in land planning projects. [ABSTRACT FROM AUTHOR]

Starting on 2 November 2023, some territories in the Region of Tuscany in Italy were hit by exceptionally intense meteorological and calamitous events. The Region of Tuscany's government was immediately at the forefront of relief, assisting the population, and aiding restoration. On 3 November, a national state of emergency was declared due to flooding. Journalistic communication is an essential aspect of disaster mitigation, preparedness, response and recovery. In terms of protecting people and reducing damage, journalists and the media have an important role to play. This article reports an Italian case study analysing the behaviour of local media in cases of natural disaster. Nine focus groups were conducted with local journalists covering the flood emergency. The results highlight the role of social and institutional mediation, rather than mere dissemination, played by the local press in emergency situations, a central element in the construction of a community bond, precisely in moments of insecurity and disorientation. The narration of a disaster from the inside seems to have allowed the emergence and representation of hitherto unknown social realities in Tuscany. The goal of the news coverage was widened, making it possible to respond better to the diverse interests of the reading public and to satisfy in a short time and exhaustively the information needs of individual communities in difficulty. [ABSTRACT FROM AUTHOR]

Earthquake hazard mapping assesses and visualizes seismic hazards in a region using data from specific points. Conducting a seismic hazard analysis for each point is essential, while continuous assessment for all points is impractical. The practical approach involves identifying hazards at specific points and utilizing interpolation for the rest. This method considers grid point spacing and chooses the right interpolation technique for estimating hazards at other points. This article examines different point distances and interpolation methods through a case study. To gauge accuracy, it tests 15 point distances and employs two interpolation methods, inverse distance weighted and ordinary kriging. Point distances are chosen as a percentage of longitude and latitude, ranging from 0.02 to 0.3. A baseline distance of 0.02 is set, and other distances and interpolation methods are compared with it. Five statistical indicators assess the methods. Ordinary kriging interpolation shows greater accuracy. With error rates and hazard map similarities in mind, a distance of 0.14 points seems optimal, balancing computational time and accuracy needs. Based on the research findings, this approach offers a cost-effective method for creating seismic hazard maps. It enables informed risk assessments for structures spanning various geographic areas, like linear infrastructures. [ABSTRACT FROM AUTHOR]

Rapid urbanization in developing countries poses challenges such as rising crime rates and resource scarcity. Unmanned Aircraft Systems (UAS) offer a promising solution to enhance public safety, but their integration requires addressing specific challenges. This study employs the Systems-Theoretic Accident Model and Processes (STAMP) and System-Theoretic Process Analysis (STPA) methodologies to identify potential hazards and requirements for integrating UAS into public safety systems in urban environments. The research objectives include identifying hazards and challenges, developing safety requirements and guidelines, and proposing strategies for efficient infrastructure investment. The proposed framework, based on STAMP/STPA, includes additional steps to consider early-stage systems and maintain stakeholder traceability. A risk matrix approach is utilized to prioritize risk mitigation measures for cost-effectiveness. The findings of this study provide valuable insights for policymakers and urban planners in developing countries seeking to harness the potential of UAS technology for enhancing public safety while addressing the unique challenges posed by rapid urbanization. [ABSTRACT FROM AUTHOR]

Floods and landslides cause continuous damage to ecosystems, infrastructures, and populations. Particularly, the occurrence and the existence of different natural hazards in the same territory highlight the need to improve risk mitigation strategies for local authorities and community resilience solutions for inhabitants. Analyzing and mapping social vulnerability provides information about the main features of a specific community to deal with natural events. Specifically, the interaction between multi-hazards and the socio-economic environment suggests multidisciplinary assessments that merge the physical and the socio-economic features of the affected territories, providing a useful approach to support multi-risk reduction planning. In this context, the article focuses on integrating landslide and flood hazard scenarios with social vulnerability in the Basilicata Region (southern Italy) at the census tract level. Thirteen municipalities were chosen as multi-hazard hot spots, while open-source platforms were selected for hazard and social vulnerability data collection and analyses. A geographic information system (GIS)-based approach was applied to combine different hazard scenarios with social vulnerability distribution among 1331 census tracts to detect the most vulnerable sub-municipality areas that need special attention in multi-risk reduction strategies. The results are presented in the form of maps, which provide a relevant suitable tool in local emergency planning. [ABSTRACT FROM AUTHOR]

Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction. [ABSTRACT FROM AUTHOR]

Many communities along rivers in the Northwest Territories do not have water-level gauges, making flood hazard analyses difficult at these sites. These include the communities of Jean Marie River, Tulita and Fort Good Hope on the Mackenzie River, Nahanni Butte on the Liard River and Fort McPherson on the Peel River. However, gauges do exist at other sites upstream and downstream of these communities, from which flood hazard assessments can be extrapolated to the ungauged communities. Reach-based extrapolation becomes particularly challenging when analysing ice-jam flood hazards since data sparsity is an additional challenge at these locations. A simple empirical approach using non-dimensional stage and discharge was implemented, which allowed only a minimum of the required data from all sites to be extracted. From the gauged sites, water-surface elevations and slopes from digital elevation models, channel widths, thalweg elevations and ice thicknesses from under-ice flow measurement surveys and recorded water levels were obtained. As a test case, results from the gauged reach of Fort Simpson were extrapolated to the ungauged reach of Jean Marie River and are presented in this technical note. [ABSTRACT FROM AUTHOR]

The health of urban populations is increasingly at risk due to the amplification and chronification of urban heat stress by climate change. This is particularly true for urban environments in humid tropical climates, including many cities in Southeast Asia. It is also in these locations where increasing climatic risks may be exacerbated by urban growth, underscoring the need to develop effective mitigation strategies for strengthening urban resilience and supporting climate change adaptation. Conservation and widespread implementation of green infrastructure (GI) are regarded as one means to counter heat as a public health threat. However, for lower-income countries across Southeast Asia, such as Vietnam, knowledge gaps remain with respect to the effectiveness of greening interventions for heat mitigation. To address this gap, in the context of urban expansion in the humid tropical city of Huế, Vietnam, diurnal cooling potential and regulation of outdoor thermal comfort (OTC) within a wide, shallow street canyon were systematically assessed for selected elements of GI along a quantitative and qualitative dimension using ENVI-met. Tree-based interventions were found to be most effective, potentially decreasing UTCI by −1.9 K at the domain level. Although lower in magnitude, green verges and green facades were also found to contribute to OTC, with green verges decreasing UTCI by up to −1.7 K and green facades by up to −1.4 K locally. Potential synergistic cooling impacts were identified through a combination of GI elements. However, no scenario was found to decrease heat stress to zero or moderate levels. Substantially reducing heat stress may thus require further measures and a closer consideration of local morphological characteristics. [ABSTRACT FROM AUTHOR]

In the maritime domain, hydrogen fuel cell propulsion and autonomous vessels are two important issues that are yet to be implemented together because of a few challenges. It is obvious that there are several individual safety studies on Maritime Autonomous Surface Ships and hydrogen storage as well as fuel cells based on various risk assessment tools but the combined safety studies that include hydrogen fuel cells on autonomous vessels with recent risk analysis methods are extremely limited. This research chooses the "System-Theoretic Process Analysis" (STPA) method which is a recent method for potential risk identification and mitigation. Both hydrogen and autonomous vessels are analyzed and assessed together with the STPA method. Results are not speculative but rather flexible compared to conventional systems. The study finds a total of 44 unsafe control actions (UCAs) evolved from human and central control unit controllers through STPA. Further, the loss scenarios (LS) are identified that lead to those UCAs so that loss scenarios can be assessed and UCAs can be mitigated for safe operation. The objective of this study is to ensure adequate safety for hydrogen fuel cell propulsion on autonomous vessels. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future. [ABSTRACT FROM AUTHOR]

The Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System, abbreviated CyCLOPS, is a national strategic research infrastructure devoted to systematically studying geohazards in Cyprus and the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region. Amongst others, CyCLOPS comprises six permanent sites, each housing a Tier-1 GNSS reference station co-located with two calibration-grade corner reflectors (CRs). The latter are strategically positioned to account for both the ascending and descending tracks of SAR satellite missions, including the ESA's Sentinel-1. As of June 2021, CyCLOPS has reached full operational capacity and plays a crucial role in monitoring the geodynamic regime within the southeastern Mediterranean area. Additionally, it actively tracks landslides occurring in the western part of Cyprus. Although CyCLOPS primarily concentrates on geohazard monitoring, its infrastructure is also configured to facilitate the radiometric calibration and geometric validation of Synthetic Aperture Radar (SAR) imagery. Consequently, this study evaluates the performance of Sentinel-1A SAR by exploiting the CyCLOPS network to determine key parameters including spatial resolution, sidelobe levels, Radar Cross-Section (RCS), Signal-to-Clutter Ratio (SCR), phase stability, and localization accuracy, through Point Target Analysis (PTA). The findings reveal the effectiveness of the CyCLOPS infrastructure to maintain high-quality radiometric parameters in SAR imagery, with consistent spatial resolution, controlled sidelobe levels, and reliable RCS and SCR values that closely adhere to theoretical expectations. With over two years of operational data, these findings enhance the understanding of Sentinel-1 SAR product quality and affirm CyCLOPS infrastructure's reliability. [ABSTRACT FROM AUTHOR]

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is "first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions". This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall. [ABSTRACT FROM AUTHOR]

The development height of the water-conducting fracture zone (WCFZ) is crucial for the safe production of coal mines. The back-propagation neural network (BP-NN) can be utilized to forecast the WCFZ height, aiding coal mines in water hazard prevention and control efforts. However, the stochastic generation of initial weights and thresholds in BP-NN usually leads to local optima, which might reduce the prediction accuracy. This study thus invokes the excellent global optimization capability of the Improved Radial Movement Optimization (IRMO) algorithm to optimize BP-NN. The influences of mining thickness, coal seam depth, working width, and hard rock lithology proportion coefficient on the height of WCFZ are investigated through 75 groups of in situ data of WCFZ heights measured in the Jurassic coalfield of the Ordos Basin. Consequently, an IRMO-BP-NN model for predicting WCFZ height in the Jurassic coalfield of the Ordos Basin was constructed. The proposed IRMO-BP-NN model was validated through monitoring data from the 4−2216 working faces of Jianbei Coal Mine, followed by a comparative analysis with empirical formulas and conventional BP-NN models. The relative error of the IRMO-BP-NN prediction model is 4.93%, outperforming both the BP-NN prediction model, the SVR prediction model, and empirical formulas. The results demonstrate that the IRMO-BP-NN model enhances the accuracy of predicting WCFZ height, providing an application foundation for predicting such heights in the Jurassic coalfield of the Ordos Basin and protecting the ecological environment of Ordos Basin mining areas. [ABSTRACT FROM AUTHOR]

Airborne pollutants pose a significant threat in the occupational workplace resulting in adverse health effects. Within the Industry 4.0 environment, new systems and technologies have been investigated for risk management and as health and safety smart tools. The use of predictive algorithms via artificial intelligence (AI) and machine learning (ML) tools, real-time data exchange via the Internet of Things (IoT), cloud computing, and digital twin (DT) simulation provide innovative solutions for accident prevention and risk mitigation. Additionally, the use of smart sensors, wearable devices and virtual (VR) and augmented reality (AR) platforms can support the training of employees in safety practices and signal the alarming concentrations of airborne hazards, providing support in designing safety strategies and hazard control options. Current reviews outline the drawbacks and challenges of these technologies, including the elevated stress levels of employees, cyber-security, data handling, and privacy concerns, while highlighting limitations. Future research should focus on the ethics, policies, and regulatory aspects of these technologies. This perspective puts together the advances and challenges of Industry 4.0 innovations in terms of occupational safety and exposure assessment, aiding in understanding the full potential of these technologies and supporting their application in industrial manufacturing environments. [ABSTRACT FROM AUTHOR]

In highway reconstruction and expansion projects, the impact of the abandoned dreg body produced by the original highway on the new bridge project cannot be ignored. If the abandoned dregs are not handled properly, they will cause great threat to the operation of the new highway as well as to the safety of the surrounding environment and personnel. In this paper, based on a South China highway reconstruction and expansion project, a model of the abandoned dreg body, bridge pile and anti-slide pile is established with the help of finite difference software FLAC3D 5.0. The calculation results show that compared with burying the anti-slide pile, slope cutting is more effective for improving the stability of the abandoned dreg body. Under the action of vertical force intensity, the displacement of the top of the pile increases continuously, and the maximum bending moment of the pile body increases firstly and then decreases. The pile displacement and bending moment of the bridge pile increase significantly in the storm condition compared with the normal condition. In addition, the abandoned dreg body hazard prevention and control technical measures are studied. The impacts of different locations of anti-slide pile burial on bridge piles are analyzed and compared, and it is determined that the optimal location of anti-slide pile burial is the middle of the secondary slope. [ABSTRACT FROM AUTHOR]

Fires are a growing problem even in temperate climate areas, such as those in Central Italy, due to climate change leading to longer and longer periods of drought. Thus, knowing the fire susceptibility of an area is crucial for good planning and taking appropriate countermeasures. In this context, it was decided to use only causal factors of a geomorphological and environmental nature in order to obtain a fire susceptibility analysis that can also be applied to climatically under-sampled areas. Vector data of fires in Central Italy from 2005 to 2023 were collected, and the correct areal extent was calculated for each. At the same time, six factors were selected that could have an influence on fire development, such as ecological units, topographic wetness index (TWI), geology, slope, exposure, and altitude. The model was obtained by means of the weight of evidence statistical method, which takes into account past data by reinterpreting them in a future-oriented way on the basis of the identified factors and classes. The model was validated with a test sample and shows an area under the curve (AUC) value of 0.72 with a reliability that can be described as good considering the total absence of climatic factors that are known to play a major role in fire development. Furthermore, the identified causal factors were divided into classes, and these were carefully weighted in order to define their relative influence in the study area. Particularly Ecological Units with characteristic and well-defined contrast (C) values, which could lead to a more complete definition of forests that tend to increase fire susceptibility and those that tend to decrease it, allowing the latter to be exploited as a hazard mitigation agent. [ABSTRACT FROM AUTHOR]

In recent years, with the acceleration of urbanization and the frequent occurrence of extreme weather globally, the risk of urban flood disasters has gradually increased, and its potential consequences are immeasurable. Therefore, conducting risk assessment of urban flood disasters is of great significance, as it is one of the foundations and decision-making means for Disaster Prevention and Mitigation, and has become a hot topic and trend in current research. This paper starts by exploring the concept and formation mechanism of urban flood disasters, taking Hazard Factors, Disaster-prone Environment sensitivity, Vulnerability of Exposed Bodies, and Disaster Prevention and Mitigation Capabilities as primary indicators. Based on this, a risk assessment index system is established with 14 secondary indicators, such as annual average rainfall, distance to water systems, elevation, and terrain undulation. The spatialization of each indicator data point is processed through ArcGIS10.7, and the importance of hazard and sensitivity indicators is ranked using the Random Forest algorithm. The indicators are then weighted using a combination of the Analytic Hierarchy Process (AHP) and the entropy method, and the combined weights of each assessment indicator are calculated. Taking Wuhan City as the research area, the weights of each indicator are input into the established risk assessment model. ArcGIS spatial analysis techniques and raster calculation functions are utilized to solve the fuzzy comprehensive evaluation of the assessment model, obtaining zoning maps of risk levels for hazard, sensitivity, vulnerability, disaster prevention, and mitigation capabilities, as well as the distribution of comprehensive risk levels. The validity and rationality of the model results are verified by actual disaster data, providing important reference for urban flood disaster prevention in the future. [ABSTRACT FROM AUTHOR]

Program management is an important strategy for organizing and managing multiple interdependent construction projects to achieve strategic goals. However, when change orders occur, they can have a serious impact on the quality, time, cost of projects and, ultimately, affect the construction program. Furthermore, when change orders are caused particularly by legislative changes, such as environmental laws, taxes, tolls, safety codes, transportation, design or building codes, their impacts are unavoidable, yet can be managed through mitigation strategies. The existing literature only reports the implications of change orders on the project level and reports legislative changes as one of the contributing factors to change orders, but does not consider the implications on a program level. This study aims to close this knowledge gap by assessing the implications of change orders caused by legislative changes on program management in the construction industry during the construction phase, and explore what the possible mitigation strategies to manage change orders caused by legislative changes are. The objectives of the study include identifying the implications of change orders on construction projects in the UAE through a literature review using peer-reviewed journals and reliable industry sources. Additionally, we investigate the implications of change orders caused by legislative changes on construction programs through interviewing subject matter experts, evaluating the importance of the reported impacts, with possible mitigation strategies, through a structured questionnaire and Relative Importance Index (RII) and, finally, proposing a set of recommendations for key industry stakeholders. A mixed methods approach is adopted in this qualitative study, and the participants include clients, contractors and consultants from the construction industry, with a defined scope covering the construction stage only. The outcomes of the study can guide program managers, decision-makers and practitioners in the construction industry to successfully deliver all projects by directing proper resources to accommodate legislative changes. [ABSTRACT FROM AUTHOR]