Search Results (115)

Accurately assessing collapse risks of high-elevation, concealed rock mass blocks within the steep cliffs of Bijiashan, Three Gorges Reservoir Area, is challenging. This study employed a multidimensional approach—integrating airborne Light Detection and Ranging (LiDAR), the transient electromagnetic method (TEM), close-range photogrammetry, horizontal drilling, and borehole optical imaging—to characterize the rock mass structure of the WD1 cliff belt and delineate 52 individual blocks. Stability analysis incorporated stereographic projection for macro-scale assessment and employed mechanical models specific to three primary failure modes (toppling, sliding, falling). Finite element strength reduction quantified the stress–strain response of a representative block under natural and rainstorm conditions. Particle Flow Code (PFC) simulated dynamic instability of the exceptionally large block W1-37. Results indicate the WD1 rock mass is highly fractured, with base sections prone to weakness. Toppling failure dominates (90.4%). Under rainstorm conditions, the average Factor of Safety (FOS) decreased by 14.7%, and 73.1% of the blocks that were stable under natural conditions were destabilized—specifically transitioning to marginally stable or substable states—often triggering chain-reaction instability characterized by “crack propagation—base buckling”. W1-37 exhibited staged failure under rainstorm: “strain localization at fissure tips—penetration of basal cracks—overturning of the upper rock mass”. Its frontal rock reached a peak sliding velocity of 15.17 m/s, indicative of base-breaking toppling. The integrated “multi-technology survey—multi-method evaluation—multi-scale simulation” framework provides a quantitative basis for risk assessment of rock mass disasters in the Three Gorges Reservoir Area and offers a technical paradigm for similar high-steep canyon regions. Full article

With the rapid development of the Internet of Things (IOT), 5G, and modern power systems, demand-side loads are becoming increasingly observable and remotely controllable, which enables demand-side flexibility to participate more actively in grid dispatch and emergency support. Under extreme rainstorm conditions, however, component failure risk rises and the availability and dispatchability of demand-side flexibility can change rapidly. This paper proposes a risk-aware emergency regulation framework that translates rainstorm information into actionable multi-load aggregation decisions for urban power systems. First, demand-side resources are quantified using four response attributes, including response speed, response capacity, maximum response duration, and response reliability, to enable a consistent characterization of heterogeneous flexibility. Second, a backpropagation (BP) neural network is trained on long-term real-world meteorological observations and corresponding reliability outcomes to estimate regional- or line-level fault probabilities from four rainstorm drivers: wind speed, rainfall intensity, lightning warning level, and ambient temperature. The inferred probabilities are mapped onto the IEEE 30-bus benchmark to identify high-risk areas or lines and define spatial priorities for emergency response. Third, guided by these risk signals, a two-level coordination model is formulated for a load aggregator (LA) to schedule building air conditioning loads, distributed photovoltaics, and electric vehicles through incentive-based participation, and the resulting optimization problem is solved using an adaptive genetic algorithm. Case studies verify that the proposed strategy can coordinate heterogeneous resources to meet emergency regulation requirements and improve the aggregator–user economic trade-off compared with single-resource participation. The proposed method provides a practical pathway for risk-informed emergency regulation under rainstorm conditions. Full article

This study investigates rainstorm-induced highway subgrade slope collapses in the coastal areas of Southeast China. By integrating the seepage–stress coupled finite element method with the strength reduction method, we simulate the entire process of seepage, deformation, and slope collapse under rainstorm conditions, analyzing the variation in the stability factor. The key findings are as follows: (1) During rainstorms, water infiltration increases soil saturation and pore water pressure, while reducing matrix suction and soil shear strength, leading to soil softening. (2) The toe of the subgrade slope first undergoes plastic deformation under rainstorms, which develops upward, and finally the plastic zone connects completely, causing collapse. The simulated landslide surface is consistent with the actual one, revealing the collapse mechanism of the subgrade slope. Additionally, the simulated displacement at the slope toe when the plastic zone connects provides valuable insights for setting warning thresholds in landslide monitoring. (3) The stability factor of the subgrade slope in the case study decreased from 1.24 before the rainstorm to 0.985 after the rainstorm, indicating a transition from a stable state to an unstable state. (4) Parameter analysis shows that heavy downpour or downpour will cause the case subgrade slope to enter an unstable state. The longer the rainfall duration, the lower the stability factor. Analysis of soil parameters indicates that strength parameters, internal friction angle, and effective cohesion exert a significant influence on slope stability, whereas deformation parameters, elastic modulus, and Poisson’s ratio have a negligible effect. Slope collapse can be timely forecasted by predicting the stability factor. Full article

The response mechanisms of reinforced soil retaining wall structures under rainfall infiltration are not fully understood, leading to insufficient design guidelines. This study investigates a modular reinforced soil retaining wall model employing concrete blocks as the facing panel, standard sand as backfill, and a biaxial geogrid as reinforcement. Three sets of tests with varying rainfall intensities (150 mm/h, 300 mm/h, and 450 mm/h) were conducted to analyse and compare the response patterns of rainwater infiltration, earth pressure distribution, reinforcement strain, and displacement characteristics. The results indicate that with increasing rainfall intensity, the depth of infiltration influence extends across the entire wall section, with significant water accumulation at the base under heavy rainstorm conditions. The distribution pattern of static earth pressure is generally consistent across tests; however, a notable abrupt change reaching 8.59 kPa was observed at mid-height under rainstorm conditions. The strain increment distribution in the reinforcement is non-uniform, with increments under heavy rain and heavy rainstorm conditions being less than those under rainstorm conditions. The displacement of the wall panel is greatest in the middle and upper sections, with the smallest displacement occurring under rainstorm conditions. The displacement pattern shows a negative correlation with both the static earth pressure and the reinforcement strain patterns. These findings provide theoretical support for drainage design and stability control of reinforced soil retaining walls in regions experiencing heavy rainfall. Full article

Natural hazards exert a detrimental influence on human survival, environmental conditions and society. Historical hazard events have generated a broad corpus of literature addressing the spatiotemporal extent, dissemination or social responses. With regard to quantitative analysis based on information locked within verbose text, the release of such information from the narrative format is encouraging. Natural Language Processing (NLP), a technique demonstrated to be capable of automated data extraction, provides a useful tool in establishing a structured dataset on hazard occurrences. In our study, we utilize scattered textual records of historical natural hazard events to create a novel dataset and explore the applicability of NLP in parallel. We put forward a standard list of toponyms based on manual annotation of a compilation of disaster-related texts, all of which were references in an authoritative publication in the field. The final natural hazards dataset comprised location data, which referred to a specific hazard report in Europe during 1301–1500, together with its geocoding result, year of occurrence and detailed event(s). We evaluated the performance of four pre-trained geoparsing tools (Flair, Stanford CoreNLP, spaCy and Irchel Geoparser) for automated toponym extraction in comparion with the standard list. All four tested methods showed a high precision (above 0.99). Flair had the best overall performance (F1 score 0.89), followed by Stanford CoreNLP (F1 score 0.83) and Irchel Geoparser (F1 score 0.82), while spaCy had a poor recall (0.5). Then we divided natural hazards into six categories: extreme heat, snow and ice, wind and hails, rainstorms and floods, droughts, and earthquakes. Finally, we compared our newly digitized natural hazard dataset to a geocoded version of the dataset provided by Harvard University, thus providing a comprehensive overview of the spatial–temporal characteristics of European hazard observations. The statistical outcomes of the present investigation demonstrate the efficacy of NLP techniques in text information extraction and hazard dataset generation, offering references for collaborative and interdisciplinary efforts. Full article

Climate change increases the intensity and frequency of extreme rainfall. Heavy rain is one of the main input sources for the complex water resources system in the watershed. Understanding its regional spatial correlation is of vital importance for promoting sustainable disaster management in the watershed. The Qiantang River Basin is a significant ecological and economic area in the Yangtze River Delta, yet systematic research on its multi-regional rainstorm-dependent structure remains insufficient. In this study, hourly rainfall data of the basin from 1950 to 2024 were used to construct marginal functions by using the peaks-over-threshold and the generalized Pareto distribution, and a mixed Copula model was established to describe the dependence structure of multi-regional extreme rainfall events. The model has been tested by RMSE and Cramér–von Mises statistics and shows reliable performance. The study reveals that the basin has a “double cluster” spatial pattern: the internal conditions of northern clusters (Hangzhou–Shaoxing) and southern clusters (Jinhua–Lishui–Quzhou) showed a strong dependence. On the contrary, under cluster conditions with low inter-regional dependence, all high-probability combinations occurred within the clusters, not outside them. This finding provides quantitative support for optimizing trans-regional emergency response, improving flood control resilience, and realizing precise allocation of resources, and is of great significance for promoting sustainable watershed governance. Full article

An accurate and effective peach recognition algorithm is a key part of automated picking in orchards; however, the current peach recognition algorithms are mainly targeted at bare fruit scenarios and face challenges in recognizing flat peach bagged fruits, based on which this paper proposes a model for recognizing and detecting flat peach fruits in complex orchard environments after bagging, namely, YOLOv8n-CDDSh. First, to effectively deal with the problem of the insufficient detection capability of small targets in orchard environments, the dilation-wise residual (DWR) module is introduced to enhance the model’s understanding of semantic information about small target defects. Second, in order to improve the detection ability in complex occlusion scenarios, inspired by the idea of large kernel convolution and cavity convolution in the Dilated Reparam Block (DRB) module, the C2f-DWR-DRB architecture is built to improve the detection ability in occluded target regions. Thirdly, in order to improve the sensitivity and precision of aspect ratio optimization, and to better adapt to the detection scenarios of targets with large differences in shapes, the ShapeIoU loss function is used to improve the fruit localization precision. Finally, we validate the effectiveness of the proposed method through experiments conducted on a self-constructed dataset comprising 1089 samples. The results show that the YOLOv8n-CDDSh model achieves 92.1% precision (P), 91.7% Mean Average Precision (mAP), and a model size of 5.73 MB, with improvements of +1.5 pp (Precision) and +0.5 pp (mAP) over YOLOv8n, respectively. In addition, the detection performance is excellent in actual orchard environments with different light angles, shading conditions, and shooting distances. Meanwhile, YOLOv8n-CDDSh deployed on the edge computing device achieved precision = 87.04%, mAP = 91.71%, and FPS = 37.20, and can also maintain high precision in bagged fruit recognition under extreme weather simulations such as fog and rainstorms, providing theoretical and methodological support for the automated picking of bagged peaches. Full article

With the vigorous development of contemporary clean energy, the participation rate of photovoltaic (PV) power generation in the whole power system is increasing day by day, and accurate PV power prediction technology is crucial for the optimal scheduling of the power system. However, the frequent occurrence of extreme climate in recent years has caused greater disturbance to PV power generation, which greatly increases the degree of difficulty in accurately predicting PV power generation and thus affects the security, economy, reliability and stability of grid system operation. In order to predict PV power under extreme climatic conditions, we firstly elaborate the PV power prediction methods and their respective advantages and disadvantages for sand, dust, rainstorm and snowfall in existing studies, and on this basis, we propose the Gray Wolf Optimization for Short-Term Forecasting Models of the Long and Short-Term Memory Model based on K-Means clustering, which ensures the accuracy of PV power prediction under extreme climatic conditions. power prediction accuracy under extreme climate conditions. Firstly, the K-means clustering algorithm is utilized to perform weather typing, which is divided into four weather categories, namely, dusty weather, heavy rain, heavy snow and normal weather. Then, for the weather typing results, the prediction effects of the Gray Wolf Optimization Long Short-Term Memory Network (GWO-LSTM) Model, Random Forest (RF) Model, Multilayer Feedforward Neural Network (BP) Model, and Long and Short-Term Memory Network (LSTM) Model are compared, respectively. The prediction results indicate that GWO-LSTM achieves the highest forecasting accuracy, with a mean root mean square error (RMSE) of 0.6235 across all four weather scenarios. Its prediction accuracy reaches approximately 95%, providing effective data support for the safe and stable operation of new power systems featuring high proportions of grid-connected photovoltaic generation. Full article

Preferential flow, which primarily drains via vertical and interconnected macropores under gravity, allows water and solutes to transport non-uniformly through the soil matrix. Such a feature exacerbates the leaching risk of pollutants to groundwater. However, there is still a lack of knowledge of how the soil macropores affect the migration of manure-sourced veterinary antibiotics (VAs) in agricultural soils. This study used a series of techniques, including field dye tracing experiments, measurements of soil water retention curves (SWRCs), and micro-CT scanning, to explore macropore characteristics for a typical Entisol. The leaching behavior of sulfadiazine (SDZ) and sulfamethazine (SMZ) was then investigated using undisturbed columns (15 cm ID × 20 cm) under simulated rainfall. The results revealed the great lateral diffusion ability of the soil (up to 65 cm) as compared to vertical penetration (50 cm depth) in the field. The soil was abundant in macropores with equivalent diameter > 200 µm, and the macroporosity was higher in the lower layer (40–60 cm) than the upper layers, where cultivation may lead to the fragmentation of the soil structure and the formation of more isolated pores. Breakthrough curves (BTCs) and hydrological modeling indicated a faster penetration time and greater leaching of sulfonamides with increased macropores in the soil. Such an effect was, however, strengthened under rainstorm conditions (25 mm h⁻¹). Antibiotics leaching was strongly correlated with the mean macropore diameter (MD), compactness (CP), and connectivity (Γ) parameters and significantly affected by MD and CP (p < 0.05), particularly at a moderate rainfall intensity (11 mm h⁻¹). This study has linked antibiotics migration with the soil structure and highlighted macropores’ contribution to their accelerated leaching, thus providing evidence for environmental risk assessments and promoting sustainable soil and water management in real scenarios of soil macropore flow. Full article

The Loess Plateau is one of the most landslide-prone regions in China, where rainfall-induced shallow loess landslides severely constrain regional economic and social development. Therefore, investigating the stability of shallow loess slopes under rainfall conditions is of great significance. Taking Yuzhong County in Gansu Province as an example, this study uses the SINMAP model (Version 2.0) to assess slope stability. The areas of unstable zones under different rainfall intensities were identified, and the spatial distribution of hazard sites was analyzed to evaluate the applicability of this deterministic physical model in the study area. Furthermore, a Personnel Risk Level (PRL) determined by combining population density with the Stability Index (SI, defined as the probability that the factor of safety exceeds 1: SI = Prob (FS > 1)) was proposed and applied to assess the potential impact of landslides on local residents. The novelty of this study lies in three aspects: (1) targeting Yuzhong County (a loess region with scarce comprehensive landslide risk assessments) to fill the regional research gap, (2) quantifying PRL through a modified hazard index (HI = population density × (1/SI)) to achieve spatialized risk mapping for vulnerable populations, and (3) systematically analyzing the dynamic response of slope stability to five gradient rainfall intensities (from light rain to severe rainstorm) and verifying model sensitivity to key parameters. The results show that as rainfall intensity increases, stable areas gradually decrease while unstable areas expand, with stable zones progressively transforming into unstable ones. Greater rainfall intensity also leads to an increase in the number of landslides within unstable zones. The proposed PRL helps delineate the severity of hazards in different townships, providing new references for mitigating casualties and property losses caused by landslides. Full article

Cloud liquid water content (CLWC) based on microwave radiometer data was investigated in this study. First, its consistency with radiosonde-based CLWC was established. Integrated CLWC was also checked against the liquid water path. CLWC performance in four weather types was considered: dense fog, clouds in spring, rainstorms, and typhoons. CLWC provides new insights into weather events. In particular, it could be useful for nowcasting low visibility associated with sea fog. It was also found to be inversely proportional to visibility in two cases of low visibility in Hong Kong. In springtime, low-level clouds and liquid water were found to exist extensively inside clouds. In rainstorm cases, supercooled cloud liquid water was absent during heavy rain but may exist within clouds when rain stops or light rain occurs. Similar observations were made in typhoon cases, namely during the direct impact of Typhoon Wipha on Hong Kong. Supercooled cloud liquid was present when outer rainbands of the typhoon affected Hong Kong with a smaller amount of rainfall. However, when Hong Kong was hit by a typhoon’s eyewall, rain was heavier, and supercooled liquid water was absent. These features are consistent with the radiosonde-based CLWC profiles. Radiometer-based CLWC is pseudocontinuous and provides additional insight into liquid water distribution in clouds under various weather conditions. Full article

The sediment yield resulting from storm erosion has become a focal point of research and a significant area of interest in the upper reaches of the Yangtze River amid changing environmental conditions. The issue of numerous types of erosivity factors (R) in storm erosion sediment yield models, with unclear applicability. This study examines two classical types of erosivity factors: the rainfall erosivity factor (EI₃₀, Zhang Wenbo empirical formula, etc.) and runoff erosivity power. Four combinatorial forms of erosion dynamic factors, encompassing rainfall and runoff elements, were developed. Based on the rainfall, runoff and sediment data of four stations along the Fu River basin–Pingwu station, Jiangyou station, Shehong station and Xiaoheba station from 2008 to 2018, the correlation between different R factors and sediment transport in different watershed areas was studied, and the semi-monthly sediment transport model of heavy rainfall in the Fu River basin was constructed and verified. The results revealed a weak correlation between the rainfall erosivity factor and the sediment transport modulus, making it unsuitable for developing a sediment transport model. In smaller basin areas, the correlation between the combined erosivity factor and sediment transport modulus was strongest; conversely, in larger basins, the relationship between runoff erosivity power and the sediment transport model was most pronounced. The power function relationship between the erosivity factor and sediment transport modulus yielded a more accurate simulation of sediment transport during the verification period, particularly during rainstorms, surpassing that of SWAT. These findings provide a scientific basis for predicting sediment transport during storms and floods in small mountainous basins. Full article

China’s North-South Transition Zone is a critical ecological transition region, marked by complex environments, climatic sensitivity, and transitional characteristics. To investigate the effects of individual rainfall events on runoff generation and sediment yield across different slopes and land uses within this zone, the study collected data from slope runoff plots (20 m in length and 5 m in width, measured as horizontal projection) at three monitoring stations (Luoshan, Lushan, Shanzhou) between 2014 and 2023. Rainfall events were classified via K-means clustering. Regression and correlation analyses were applied to reveal the effects of rainfall characteristics, slope gradient and land use type (grass land, dry land, forest land, bare land and natural vegetation) on runoff and sediment. The results indicate that: (1) The most frequent rainstorms were Type C (short, low-intensity, low-volume, low-erosivity events). (2) The runoff depth of bare land is 3.6, 2.3, and 2 times that of forest land, dry land, and natural vegetation, respectively. Similarly, its sediment concentration is 134, 13, and 16 times higher, respectively. Grassland, however, showed markedly lower levels of both runoff and sediment. (3) Rainfall intensity was significantly correlated with runoff and sediment across slopes. Runoff depth depended mainly on rainfall amount. While Type A (prolonged, high-intensity) caused peak runoff, Type D (moderate but intense and erosive) yielded the highest sediment. (4) Sediment reduction efficiency (sediment reduction compared to bare land under identical conditions) consistently surpassed runoff reduction across all land types, with grassland showing the highest efficiency for both. For soil and water conservation, grass-planting was the most effective measure on 10° and 15° slopes, whereas both afforestation and grass-planting were optimal on the 25° slopes. Full article

In response to the increasing frequency of urban rainstorms, this study focuses on investigating the friction coefficient related to pedestrian instability under urban road flooding conditions. The objective is to conduct an in-depth analysis of the friction coefficient between pedestrians and the ground in actual flood scenarios and its variations, providing practical data to support future pedestrian safety assessments under flood conditions. Wet friction coefficient experiments were conducted under waterlogged conditions, with real human subjects tested across various operational scenarios. A buoyancy calculation formula was introduced to explore the impact of pressure changes caused by buoyancy on the human body in water, influencing the friction coefficient. An exponential relationship between pressure and the friction coefficient was established. Furthermore, by considering factors such as outsole hardness, ground type, and pressure variations with water depth, a dynamic method for selecting the friction coefficient was proposed, offering a scientific basis for determining friction coefficient thresholds associated with pedestrian instability risks. Experimental results indicate that, in the combination of hydrophilic materials with experimental asphalt and cement pavements, the friction coefficient under waterlogged conditions is generally higher than under dry conditions. However, as pressure increases, the friction coefficient of rubber materials decreases. This study concludes that the selection of the friction coefficient in pedestrian instability analysis should be treated as a dynamic process, and relying on a fixed friction coefficient for force analysis of pedestrian instability may lead to significant inaccuracies. Full article

Precipitation typically leads to the accumulation of soil moisture, which causes slope instability and triggers landslides. However, due to the lag nature of this process, landslides usually do not occur on the day of heavy rainfall. Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. In this study, six machine learning models are compared, with antecedent effective precipitation included as a conditioning factor for model training. The optimal model is selected to simulate landslide susceptibility maps under four return periods (5, 10, 20, and 50 years). Additionally, the mean decreases in the Gini and SHAP values are employed to identify the most significant factors contributing to landslides. The results indicate the following: (1) Effective antecedent precipitation is the most influential factor in landslide occurrence, ranging from one to two times higher than other factors. (2) Most meteorological stations in the study area show antecedent effective precipitation that follows a lognormal distribution, mainly in coastal areas, with a secondary fit to the general extreme value distribution. The spatial distribution of antecedent effective precipitation is more prominent in the coastal and western mountainous regions, with lower values that then increase with longer return periods in central areas. (3) The XGBoost model achieves the best performance, with an area under the curve of 0.96 and an accuracy of 89.02%. (4) The landslide susceptibility maps for the four return periods reveal three high-risk zones: the southern coastal mountains, the western Zhejiang mountains, and the areas surrounding the hilly region of Shaoxing to Taizhou in central Zhejiang. This study provides dynamic decision-making support for the prevention and control of rainstorm-induced landslide risks. Full article