Search Results (396)

Due to climate change and its increased variability, as well as the extreme weather events, flooding is becoming a major natural threat causing profound economic, social, and ecological impact. This paper systematically reviews 89 peer-reviewed articles published between 2010 and 2024 on flood risk assessment approaches, including geospatial techniques and methods for flooding, using the PRISMA framework and the ScienceDirect and Web of Science databases. GIS and remote sensing are the most popular tools for flood hazard mapping, and hydrodynamic models such as HEC-RAS and MIKE FLOOD dominate flood simulation. Machine learning algorithms, multi-criteria decision analysis (MCDA), and climate scenario analysis have also emerged as increasingly prominent methodological contributions to flood risk frameworks. This review makes a novel contribution by providing the first systematic synthesis of geospatial flood risk assessment methods, explicitly quantifying both the urban–rural research imbalance and the degree of hazard, vulnerability, and exposure integration across the literature. Specifically, only 13 (2.7%) of all eligible articles addressed rural flooding, despite the profound socioeconomic impacts that disproportionately affect these communities, and only 16% of included studies integrated any combination of hazard, vulnerability, and exposure components within current assessment approaches. This review highlights the importance of interdisciplinary collaboration and sensitivity to rural contexts in cultivating resilience and fostering equitable flood risk management. Full article

Weather-related hazards cause significant societal impacts, yet systematic long-term analyses linking these events to all levels of impact severity remain limited. This study investigates weather-related events and their associated impacts in Greece (2000–2025) using the High-Impact Weather Events Database (HIWE-DB). The HIWE-DB records 626 events, corresponding to 1871 localized records and includes 269 confirmed fatalities. Flood-related hazards are dominant, followed by windstorms, while one-third of all events involve multiple hazardous phenomena. A multilevel analysis, independently assessing weather intensity (W) and impact severity (I), reveals a statistically significant annual increase in the total number of events, driven mainly by low- to moderate-impact events (I1-I2), alongside an increase in high-intensity events (W3). While the most severe events (I3) show high annual variability, they exhibit a 38% increase in the second half of the study period compared to the first. Spatially, societal impacts are predominantly concentrated in major metropolitan areas, whereas the highest per capita fatality rates occur in specific regions, such as West Attica. The findings demonstrate how the independent indicators of intensity and severity contribute to understanding the link between weather hazards and societal exposure, providing an empirical basis for evidence-based risk assessment and impact-based early warnings. Full article

In recent years, global climate change has significantly increased the incidences of grassland fires, shifting their occurrence from seasonal events (primarily spring and autumn) to annual incidents. To enable a more accurate evaluation and zoning of grassland fire risk, this study established the Fire Source Hazard Index, Fire Fuel Hazard Index, and Fire Environmental Hazard Index based on multi-source data, employing the entropy weight method, random forest modeling, mathematical statistics, and spatial analysis. A comprehensive seasonal grassland fire hazard assessment model was constructed using these three indices and seasonal fire hazard zones were evaluated in Inner Mongolia. The results indicated that, among the fire source factors, the hazard weight of foreign fire sources was relatively high during spring (0.37) and summer (0.44). In autumn and winter, the hazard weights of road networks were higher, at 0.38 and 0.44, respectively. In the comprehensive hazard assessment, the fire environment hazard exhibited an objective existence with notable seasonal variation, whereas the hazard weight of fire source factors exceeded that of fuels across all seasons. The comprehensive grassland fire hazard in Inner Mongolia demonstrated distinct seasonality and regional heterogeneity. Temporally, fire hazards are widespread and intense in spring, limited and concentrated in summer, extensive yet dispersed in autumn, and lowest in winter. Spatially, grassland fire hazards decreased from east to west, with higher hazards concentrated in the eastern regions. Western Inner Mongolia had the lowest probability of fire occurrence. The validation results revealed a positive correlation between the proportion of fire points and hazard grades, confirming the rationality of the hazard classification and the accuracy of the assessment, which provides an important theoretical basis for the scientific management and effective prevention and control of grassland fires. Future research should further refine and explore more precise methods for grassland fire hazard assessment. Full article

Conventional structural design frameworks assess natural hazards as statistically independent phenomena, a practice that can lead to significant underestimation of risk for structures subjected to sequential or concurrent hazards. The generation of probabilistic fragility functions under such cascading loads, particularly for post-fire seismic events, presents a computational barrier for standard non-linear dynamic analysis. To address this barrier, this study introduces a comprehensive computational framework centered on a physics-constrained neural network (PCNN) to serve as a high-fidelity surrogate model. The framework first uses a non-linear 12-degree-of-freedom structural model to generate a baseline dataset of collapse times under post-fire, concurrent wind-earthquake loading via the computationally efficient endurance time (ET) method, confirming that wind effects are negligible under ambient conditions and that the framework correctly identifies this hazard hierarchy without prior labeling, while fire and seismic parameters dominate. This dataset is subsequently used to train the PCNN, which is validated to achieve exceptional predictive accuracy ($R^2=$ 0.991), performing on par with a state-of-the-art Random Forest model while enforcing physical constraints. A feature importance analysis confirmed that structural collapse is dominated by fire intensity (≈55%) and initial structural period (≈45%). The validated PCNN is then applied to demonstrate the framework’s capability, rapidly generating fragility curves that quantify the catastrophic effect of fire on seismic resilience. This analysis reveals that a severe 800 °C localized fire reduces the structure’s median collapse capacity by 94.7%, thereby establishing the proposed framework as a successful template for tackling complex, non-linear problems in multi-hazard engineering. Full article

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 PM local time, i.e., outside the typical noon to 2:00 PM detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 pm. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions.. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts. Full article

This study presents a standardized, multi-dimensional framework to evaluate flood resilience across countries using a novel Flood Index that integrates hazard severity, infrastructure vulnerability, emergency response, and recovery duration. While prior research frequently focuses on single-case studies or emphasizes hazard and vulnerability alone, systematic, multi-case comparisons that incorporate response quality and recovery timelines remain scarce. This study addresses that gap through a comparative assessment of five recent flood events: Germany (2021), Belgium (2021), Sydney (2022), Auckland (2023), and Italy (2023). The Flood Index combines quantitative metrics with AI-scored qualitative insights, drawn from policy documents and event reports, and applies multi-criteria decision-making tools for cross-case ranking. A sensitivity analysis identifies how individual dimensions influence overall resilience outcomes. The analysis reveals that Auckland performed best due to low vulnerability, efficient emergency coordination, and rapid recovery, despite moderate hazard severity. Conversely, Germany and Italy, despite strong institutional capacity, were penalized for high vulnerability and prolonged recovery phases. These results highlight the importance of balancing structural readiness, coordinated response, and recovery planning. This study contributes a replicable, cross-phased, case-study-based methodology that enables policymakers and planners to diagnose system-level weaknesses, benchmark disaster performance, and guide investments toward more adaptive, resilient infrastructure and emergency management strategies. Full article

Robotic systems are increasingly being deployed in mining operations to support tasks such as inspection, navigation, environmental monitoring, and safety supervision. However, mining environments present significant challenges for robotic perception due to dynamic terrain conditions, poor illumination, airborne dust, and frequent disturbances caused by excavation and heavy machinery. Conventional frame-based vision systems often struggle under these conditions due to motion blur, latency, and limited dynamic range. This study proposes a system-level conceptual framework for integrating event-based sensing into robotic mining systems in order to support perception in highly dynamic and safety-critical environments, with the aim of improving responsiveness and robustness under such conditions. Event-based cameras, inspired by biological vision, asynchronously detect brightness changes at the pixel level and provide microsecond temporal resolution with high dynamic range and low latency. The proposed framework combines event cameras with complementary sensing modalities including LiDAR, inertial measurement units, and RGB cameras to form a multi-sensor perception architecture. The framework is structured into multiple functional layers encompassing environmental sensing, event-driven perception, sensor fusion and AI processing, digital twin integration, and autonomous decision-making. Potential application scenarios including robotic tunnel inspection, autonomous navigation of mining robots, hazard detection, multi-agent cooperation in mining sites, and real-time digital twin updating are also discussed. The proposed framework provides a unified system-level reference architecture intended to guide future implementation and validation. Full article

Low-lying and riverine areas of Sabah and Sarawak in East Malaysia are increasingly exposed to compound flood hazards driven by intensified monsoon rainfall, sea-level rise, and land-use change. Recent projections indicate stronger extreme rainfall, fewer dry days, but more high-intensity events, and significant increases in annual rainfall and sea level, all of which elevate fluvial, pluvial, and coastal flood risk. In this study, climate-responsive vernacular architecture is investigated as a passive, low-carbon strategy for enhancing residential flood resilience in East Malaysia. Traditional stilted Malay kampung houses, Bornean longhouses, and coastal stilt settlements were explored since they have historically evolved to cope with seasonal inundation, high humidity, and tropical thermal loads. In this study, the following was conducted: (1) historical flood and climate analysis for key basins (Rajang, Sarawak, Kinabatangan); (2) morphological and typological analysis of vernacular dwellings; (3) parametric physical and hydrodynamic simulation of elevated and amphibious configurations; and (4) multi-criteria performance assessment based on structural robustness, flood safety, thermal comfort, cultural acceptability, and embodied carbon. Results from scenario-based simulations show that well-configured stilted typologies, with optimized floor elevation, breakaway panels, and porous undercroft zones, can reduce flood damage depth by 60–80% and expected annual loss by 30–55%. By translating these findings into a design guideline and decision matrix for climate-responsive housing in East Malaysia, contemporary reinterpretations of vernacular strategies were embedded into Malaysian building codes, state-level planning policies, and community-led upgrading programmes. Full article

The work environment in automated laboratories and industrial sites exposes workers to the risks associated with chemical gas and vapor leaks caused by unforeseen incidents. Such leaks may result in severe health hazards as well as damage to equipment or infrastructure at the leak site. Therefore, the development of systems capable of early detection and highly accurate localization of chemical leaks is of high importance for occupational safety. In this work, a low-cost, portable sensor node based on the Internet of Things (IoT) is proposed for the detection and localization of gas and chemical leaks in indoor environments. The sensor node features a modular design that enables flexible integration and replacement of gas and environmental sensors depending on the target application. In addition, the system includes an ultra-wideband (UWB)-based positioning and tracking unit, allowing operation across multiple indoor zones. The main contribution of this work lies in the combined integration of (i) multi-sensor-based environmental event detection and prediction and (ii) high-precision location within a dynamic multi-zone tracking architecture. The system automatically selects the most relevant anchors in each zone and applies trilateration and least-squares estimation, enhanced by Kalman filtering techniques. In particular, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are employed, with sensor fusion incorporating inertial measurement unit (IMU) data to mitigate the effects of on-line-of-sight (NLoS) conditions and signal degradation caused by obstacles. Experimental results demonstrate that both the EKF and UKF significantly reduce positioning errors and improve tracking stability compared to baseline methods under challenging indoor conditions. The UKF shows superior performance in highly nonlinear scenarios. A quantitative evaluation using manually surveyed reference points showed that the UKF achieved the best overall performance, with a mean error of 39.72 cm and an RMSE of 43.03 cm. These findings confirm the effectiveness of Kalman filter-based sensor fusion for reliable indoor positioning and highlight the suitability of the proposed system for real-time safety monitoring applications. Full article

Glacial lake dynamics in high-mountain regions serve as a sensitive proxy for cryospheric responses to climate warming. This study utilizes multi-temporal Sentinel-2 imagery and digital elevation model (DEM) data to quantify glacial lake evolution in the Donglin Tsangpo Watershed, a strategically important section of the China–Nepal Economic Corridor, from 2016 to 2024. The results show a significant expansion in both the number (from 43 to 56) and total area (from 3.97 km² to 4.94 km², +24.43%) of glacial lakes, primarily driven by the rapid emergence of very small lakes (0.02–0.05 km²) and a clear upward shift in elevation distribution, with new lakes forming above 5300 m and extending to elevations exceeding 5500 m. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) reveals that this expansion coincided with pronounced positive thermal anomalies, particularly the 2020 extreme warm event (daytime +3.88 °C, nighttime +1.61 °C). Mechanistic analysis using the ERA5-Land reanalysis dataset further demonstrates that persistent positive downward longwave radiation (LW) anomalies (peaking at +10.71 W/m² in 2021) effectively compensated for reduced shortwave input, inhibiting nocturnal refreezing and extending the effective ablation period. Furthermore, a rising liquid-to-solid precipitation ratio and extreme melt-day anomalies (up to +39.36 days) provided intensified hydrothermal inputs, driving the pronounced expansion of glacier-contact lakes despite non-linear interannual responses. This study also estimates individual lake volumes, identifying a transition toward rapid lake development that elevates potential downstream hazard exposure. These findings provide a high-resolution dataset and a robust physical framework for transboundary environmental monitoring and risk assessment in this climate-sensitive region. Full article

Pluvial floods can cause severe socio-economic impacts on coastal urban areas like the Metropolitan Area of Barcelona. This study combined the development of high-resolution flood maps, based on a large-scale coupled 1D/2D model and empirical functions, to quantify direct economic damage to buildings and determine risk to pedestrians and vehicles. Importantly, the flood model included a network of 36 municipalities and covered 636 km². Three scenarios were considered: single-hazard (extreme precipitation), multi-hazard (coincident extreme precipitation and storm surge), and adaptation (implementation of resilience measures). In total, 20 rain events were applied for each scenario: 5 were historic design storms, while 15 considered the effect of climate change (60 simulations in total). By the end of the century, results show potential increases in expected annual damage of up to 36%, from €139.8 M to €190.3 M. Risk for pedestrians could increase by 25% (494 ha to 620 ha) and for vehicles by 26% (59 km to 75 km) in the T10 single-hazard scenario. In the multi-hazard case, the socio-economic impacts are approximately 5% higher, while the adaptation simulations considering sustainable urban drainage systems show reductions between 6 and 18%. The metropolitan results were compared and validated with a previous assessment done in the City of Barcelona. Based on these results, urban planners, emergency responders, and public administrations can develop effective adaptation measures based on cost–benefit analyses for current and future climate scenarios. Compared to previous studies, this approach adapts existing urban-scale methodologies to regional-scale flood risk assessment. Full article

Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 “23·7” rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROC–AUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure. Full article

Extreme meteorological phenomena—characterized by gale-force winds, torrential rainfall, and ice-snow accumulation—pose significant threats to the operational safety of high-speed railways, with wind-induced hazards being especially critical. Such events can trigger catastrophic incidents, including train derailments and service disruptions, as evidenced by numerous documented cases worldwide. To bolster the wind resilience of high-speed railway systems, high-precision wind speed prediction has become a cornerstone for ensuring operational safety. This research presents a systematic review of international advancements in railway wind early warning systems, critically evaluating the technical attributes and performance constraints of four primary paradigms: physical numerical models, statistical methods, machine learning algorithms, and hybrid frameworks. Moving beyond a simple taxonomy, this paper delineates the strengths, limitations, and domain-specific applicability of each approach within the high-speed railways context. Furthermore, it assesses the transformative potential of emerging large-scale Artificial Intelligence (AI) meteorological models for wind speed forecasting. A quantitative comparison is provided to facilitate rigorous methodological assessment. The findings reveal four critical technical bottlenecks: (1) low computational efficiency of numerical models; (2) insufficient spatiotemporal resolution of monitoring data; (3) poor generalization of predictive models; and (4) the “black-box” nature and weak interpretability of AI models. To address these, this paper posits that future research should prioritize key technologies including multi-source heterogeneous data fusion, algorithmic optimization, design of intelligent algorithms, probabilistic risk forecasting, and the synergistic integration of AI with numerical weather prediction (NWP). Such advancements will catalyze the development of more robust HSR wind warning systems, ensuring sustained safety and operational efficiency under volatile meteorological conditions. Full article

Explosive ordnance (EO), including AXO (abandoned explosive ordnance), IEDs (improvised explosives devices), and UXO (unexploded ordnance), are widely recognised for their blast and fragmentation hazards, but they also represent a persistent and under-addressed source of occupational chemical exposure for explosive ordnance disposal (EOD) personnel. EOD core activities liberate mixed metals and energetic chemicals, resulting in exposures that are multi-route (inhalation of dusts and fumes, dermal loading amplified by sweat and glove occlusion, and ingestion via hand-to-mouth transfer during eating, drinking, or smoking) and multi-temporal (repeated low-dose background plus task-driven spikes), as well as chemically complex. Clinically, this can present as syndromic overlap across acute and chronic domains, with symptoms that are easily misattributed to heat stress, dehydration, infection, or fatigue. Acute effects of concern include neurotoxic presentations (headache, dizziness, confusion, tremor, and seizure), respiratory and mucosal irritation following dust or fume events, gastrointestinal symptoms, and patterns suggestive of acute hepatic or renal stress, particularly when high-intensity tasks occur in hot environments that compound physiologic strain. Chronic outcomes relevant to repeatedly exposed EOD personnel include renal function decline, neurocognitive effects that can degrade operational decision making and safety, persistent haematologic abnormalities, and endocrine disruption signals, with long-latency risks requiring cautious interpretation given sparse longitudinal data and confounding co-exposures. This review synthesises the current evidence base through an EOD lens and translates it into pragmatic clinical and programmatic actions: task-based exposure characterisation; tiered biomonitoring and medical surveillance aligned to operational tempo; incident-triggered assessment pathways after high-residue events; and prevention strategies that work under field constraints, including contamination control zones, hygiene enforcement, glove and respiratory protection optimisation, tool and vehicle decontamination, and measures to prevent secondary transfer and take-home exposure. The central takeaway is practical: EOD programs can reduce morbidity and improve readiness by treating explosive ordnance as a chemical mixture exposure problem, adopting mixture-aware clinical triage, and embedding surveillance and controls that match how EOD work is actually performed. Full article

Glacial lake outburst floods (GLOFs) are high-impact hazards in mountain regions, yet many events remain poorly documented because field access is limited and lake evolution can occur on sub-weekly time scales. Here, we used high spatiotemporal resolution PlanetScope imagery (3 m) to quantify the seasonal evolution and abrupt drainage of a moraine-dammed glacial lake in August 2025 in northern Pakistan. Historical lake dynamics were reconstructed using PlanetScope (2016–2024) imagery and multi-decadal Landsat observations (1992–2018). Climatic conditions were evaluated using ERA5-Land temperature data, and seasonal snow dynamics were characterized using MODIS and PlanetScope-based snow cover analyses. Multi-decadal satellite imagery indicates that lake formation in this catchment was historically intermittent, with no evidence of abrupt drainage before 2025, highlighting the anomalous nature of the event. PlanetScope observations show steady lake expansion throughout summer 2025, reaching a maximum area of 0.052 km² prior to the GLOF on August 22. Pre- and post-event imagery reveals no discernible landslide or impact trigger. Instead, the observations are most consistent with a failure mechanism driven by meltwater-driven lake growth and overtopping or erosion of the moraine dam. The 2025 summer season (June to September) was characterized by exceptionally warm conditions and unprecedented early snow depletion relative to the 2000–2024 baseline, suggesting a strong climatic and cryospheric contribution to the outburst. These results demonstrate the value of integrating dense time series of satellite observations and climatic data for capturing glacial-lake life cycles and diagnosing likely controls on outburst initiation. The study highlights the critical role of high-frequency satellite remote sensing for improving GLOF monitoring and early-warning capabilities in data-scarce mountain environments. Full article