Search Results (39)

Located on the northern side of the China–Pakistan Highway in the Pamir Plateau, Laqi Gully represents a typical rainfall–meltwater coupled debris flow gully. During 2020–2024, seven debris flow events occurred in this area, four of which disrupted traffic and posed significant threats to the China–Pakistan Economic Corridor (CPEC). The hazard assessment of debris flows constitutes a crucial component in disaster prevention and mitigation. However, current research presents two critical limitations: traditional models primarily focus on single precipitation-driven debris flows, while low-resolution digital elevation models (DEMs) inadequately characterize the topographic features of alpine narrow valleys. Addressing these issues, this study employed GF-7 satellite stereo image pairs to construct a 1 m resolution DEM and systematically simulated debris flow propagation processes under 10–100-year recurrence intervals using a coupled rainfall–meltwater model. The results show the following: (1) The mudslide develops rapidly in the gully section, and the flow velocity decays when it reaches the highway. (2) At highway cross-sections, maximum velocities corresponding to 10-, 20-, 50-, and 100-year recurrence intervals measure 2.57 m/s, 2.75 m/s, 3.02 m/s, and 3.36 m/s, respectively, with maximum flow depths of 1.56 m, 1.78 m, 2.06 m, and 2.52 m. (3) Based on the hazard classification model of mudslide intensity and return period, the high-, medium-, and low-hazard sections along the highway were 58.65 m, 27.36 m, and 24.1 m, respectively. This research establishes a novel hazard assessment methodology for rainfall–meltwater coupled debris flows in narrow valleys, providing technical support for debris flow mitigation along the CPEC. The outcomes demonstrate significant practical value for advancing infrastructure sustainability under the United Nations Sustainable Development Goals (SDGs). Full article

Granite residual soil exhibits a tendency to collapse and disintegrate upon exposure to water, displaying highly unstable mechanical properties. This makes it susceptible to landslides, mudslides, and other geological hazards. In this study, three common biopolymers, i.e., xanthan gum (XG), locust bean gum (LBG), and guar gum (GG), are employed to improve the strength and stability of granite residual soil. A series of experiments were conducted on biopolymer-modified granite residual soil, varying the types of biopolymers, their concentrations, and curing times, to examine their effects on the soil’s strength properties and failure characteristics. The microscopic structure and interaction mechanisms between the soil and biopolymers were analyzed using scanning electron microscopy and X-ray diffraction. The results indicate that guar gum-treated granite residual soil exhibited the highest unconfined compressive strength and shear strength. After adding 2.0% guar gum, the unconfined compressive strength and shear strength of the modified soil are 1.6 times and 1.58 times that of the untreated granite residual soil, respectively. Optimal strength improvements were observed when the biopolymer concentration ranged from 1.5% to 2%, with a curing time of 14 days. After treatment with xanthan gum, locust bean gum, and guar gum, the cohesion of the soil is 1.36 times, 1.34 times, and 1.55 times that of the untreated soil, respectively. The biopolymers enhanced soil bonding through cross-linking, thereby improving the soil’s mechanical properties. The gel-like substances formed by the reaction of biopolymers with water adhered to encapsulated soil particles, significantly altering the soil’s deformation behavior, toughness, and failure modes. Furthermore, interactions between soil minerals and functional groups of the biopolymers contributed to further enhancement of the soil’s mechanical properties. This study demonstrates the feasibility of using biopolymers to improve granite residual soil, offering theoretical insights into the underlying microscopic mechanisms that govern this improvement. Full article

Accurate and timely disaster classification is critical for effective disaster management and emergency response. This study proposes an improved ResNet-50-based deep learning model to classify seven types of natural disasters, including earthquake, fire, flood, mudslide, avalanche, landslide, and land subsidence. The dataset was compiled from publicly available sources and partitioned into training and validation sets using an 8:2 split. Experimental results demonstrate that the proposed model achieves a classification accuracy of 87% on the validation set and outperforms the traditional VGG16 model in most evaluation metrics, including precision, recall, F1-score, AUC, specificity, and log loss. Furthermore, the model effectively mitigates the gradient vanishing problem, ensuring stable convergence and robust training performance. These findings provide a practical technical reference for multi-disaster classification tasks and contribute to enhancing the efficiency of disaster response and societal resilience. Full article

The phenomenon of tunnel erosion is quite common in the Loess Plateau. Tunnel erosion can cause disasters such as landslides, mudslides, and ground collapses, resulting in significant economic losses and posing a threat to people’s safety. Therefore, understanding the evolution mechanism of tunnel erosion not only helps to analyze and predict the development law of erosion but also has a certain guiding role in engineering activities. Many scholars (including our team) have conducted field investigations and statistical analysis on the phenomenon of tunnel erosion in loess; however, these studies still have shortcomings in visual quantitative analysis. The combination of the Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) has significant advantages in studying soil seepage and erosion. Based on existing experimental research, this article combines the Discrete Element Method (DEM) with Computational Fluid Dynamics (CFD) to establish a CFD-DEM coupled model that can simulate tunnel erosion processes. In this model, by changing the working conditions (vertical cracks, horizontal cracks, and circular holes) and erosion water pressure conditions (200 Pa, 400 Pa, 600 Pa), the development process of tunnel erosion and changes in erosion rate are explored. The results indicate that during the process of fluid erosion, the original vertical crack, horizontal crack, and circular hole-shaped tunnels all become a circular cave. The increase in erosion water pressure accelerates the erosion rate of the model, and the attenuation rate of the particle contact force chain also increases, resulting in a decrease in the total erosion time. During the erosion process, the curve of the calculated erosion rate shows a pattern of slow growth at first, then rapid growth, before finally stabilizing. The variation law of the erosion rate curve combined with the process of tunnel erosion can roughly divide the process of tunnel erosion into three stages: the slow erosion stage, the rapid erosion stage, and the uniform erosion stage. Full article

Debris flow in the upper Yellow River is very developed and is generally characterized by wide distribution with large numbers and a high frequency of occurrence. This paper analyses the distribution characteristics, material composition, and formation causes of the Erlian debris flow fan in the eastern part of the Guide Basin and discusses the relationship between debris flow fan and river evolution. Results show that: (1) At least 66 debris flow gullies and 20 large debris flow accumulation fans have been developed on both sides of the Yellow River in the eastern Guide Basin. (2) In the Erlian Village area, the Yellow River channel has experienced the accumulation, erosion, destruction, and accumulation process of debris flow fans in 16 kaB.P., 16 ka B.P.–8 ka B.P., and 8 kaB.P., respectively, the late-accumulation fan has been continuously extruding the Yellow River channel since 8 kaB.P., and the Yellow River channel has been shifted to the south by at least 1.25 km during the period of 8 ka. (3) Five accumulation periods for the Late Mudslide Fan were identified by classifying the 16 kaB.P. and 8 kaB.P. early and late mudslide fans. This study can provide theoretical and technical support for preventing debris flow disasters in the upper reaches of the Yellow River and has certain reference and reference values. Full article

The Sichuan Basin in China is one of the most concentrated areas of red beds in China. In the red-bed area, abundant rainfall can easily cause natural disasters, such as landslides, mudslides, collapses, and subsidence. This has had a great impact on the safety of people and property and sustainable modernization in the area. Zhongjiang County of Sichuan Province is a typical red-bed area, and red sandstone is one of the main foundation rocks in this area. Under the influence of rainfall, the strength of red sandstone foundation easily decays, causing disasters such as house collapse. Therefore, in order to explore the influence of rainfall on the mechanical properties of red sandstone, this paper takes the red sandstone in Zhongjiang County, Sichuan Province, China, as the research object and conducts acoustic-emission uniaxial compression experiments under different water contents. The strength characteristics, instability precursor characteristics, fracture types, and damage characteristics of red sandstone in different water-bearing states are obtained. The abovementioned results provide a reference for the Zhongjiang County Government to consider the impact of rainfall on the red sandstone foundation during modernization and emergency management. Full article

In disaster-stricken areas that were severely damaged by earthquakes, typhoons, floods, mudslides, and the like, employing unmanned aerial vehicles (UAVs) as airborne base stations for mobile edge computing (MEC) constitutes an effective solution. Concerning this, we investigate a 3D air–ground collaborative MEC scenario facilitated by multi-UAV for multiple ground devices (GDs). Specifically, we first design a 3D multi-UAV-assisted air–ground cooperative MEC system, and construct system communication, computation, and UAV flight energy consumption models. Subsequently, a cooperative resource optimization (CRO) problem is proposed by jointly optimizing task offloading, UAV flight trajectories, and edge computing resource allocation to minimize the total energy consumption of the system. Further, the CRO problem is decoupled into two sub-problems. Among them, the MATD3 deep reinforcement learning algorithm is utilized to jointly optimize the offloading decisions of GDs and the flight trajectories of UAVs; subsequently, the optimal resource allocation scheme at the edge is demonstrated through the derivation of KKT conditions. Finally, the simulation results show that the algorithm has good convergence compared with other algorithms and can effectively reduce the system energy consumption. Full article

Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the materials involved. Indeed, rockfalls are generated when rocks fall from a very steep slope, while debris flow and/or mudslides are generated by fine materials like silt and clay after strong water imbibition. These events can amplify the damage caused by the initial trigger and propagate instability along a slope, often resulting in significant environmental and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici Mountains along the border of the Abruzzo and Lazio regions (Italy), serves as a notable example of the complexities and devastating consequences associated with such events. In March 2021, a substantial debris flow event obstructed the Liri River, marking the latest step in a series of landslide events. Conventional techniques such as geomorphological observations and geological surveys may not provide exhaustive information to explain the landslide phenomena in progress. For this reason, UAV image acquisition, InSAR interferometry, and pixel offset analysis can be used to improve the knowledge of the mechanism and kinematics of landslide events. In this work, the interferometric data ranged from 3 January 2020 to 24 March 2023, while the pixel offset data covered the period from 2016 to 2022. The choice of such an extensive data window provided comprehensive insight into the investigated events, including the possibility of identifying other unrecorded events and aiding in the development of more effective mitigation strategies. Furthermore, to supplement the analysis, a specific finite element method for slope stability analysis was used to reconstruct the deep geometry of the system, emphasizing the effect of groundwater-level flow on slope stability. All of the findings indicate that major landslide activities were concentrated during the heavy rainfall season, with movements ranging from several centimeters per year. These results were consistent with numerical analyses, which showed that the potential slip surface became significantly more unstable when the water table was elevated. Full article

This longitudinal study examines young adult mental health (MH) trajectories after exposure to natural disasters (i.e., hurricanes, wildfires, mudslides) across four waves, two pre- and two during the COVID-19 pandemic. Participants (n = 205) answered questions about anxiety, depression, and post-traumatic stress symptoms (PTSSs) across Waves (Ws) s 1–4 and pre-pandemic factors (prior trauma history, disaster exposure, life stressors since disaster) at Wave (W) 1. Hierarchical linear modeling was conducted to examine MH trajectories and associations with pre-pandemic factors. Only the PTSS trajectory significantly differed across all Ws, with the largest increase between Ws 2 and 3 (pre- and during-pandemic time points). Prior trauma history and life stressors since the disaster were significantly associated with all MH trajectory intercepts but not growth rates. Full article

The 2017 Jiuzhaigou earthquake, registering a magnitude of 7.0, triggered a series of devastating geohazards, including landslides, collapses, and mudslides within the Jiuzhaigou World Natural Heritage Site. These destructive events obliterated extensive tracts of vegetation, severely compromising carbon storage in the terrestrial ecosystems. Net Primary Productivity (NPP) reflects the capacity of vegetation to absorb carbon dioxide. Accurately assessing changes in NPP is crucial for unveiling the recovery of terrestrial ecosystem carbon storage after the earthquake. To this end, we designed this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Productivity datasets. The findings are as follows. NPP in the co-seismic landslide areas remained stable between 525 and 575 g C/m² before the earthquake and decreased to 533 g C/m² after the earthquake. This decline continued, reaching 483 g C/m² due to extreme rainfall events in 2018, 2019, and 2020. Recovery commenced in 2021, and by 2022, NPP had rebounded to 544 g C/m². The study of NPP recovery rate revealed that, five years after the earthquake, only 18.88% of the co-seismic landslide areas exhibited an NPP exceeding the pre-earthquake state. However, 17.14% of these areas had an NPP recovery rate of less than 10%, indicating that recovery has barely begun in most areas. The factor detector revealed that temperature, precipitation, and elevation significantly influenced NPP recovery. Meanwhile, the interaction detector highlighted that lithology, slope, and aspect also played crucial roles when interacting with other factors. Therefore, the recovery of NPP is not determined by a single factor, but rather by the interactions among various factors. The ecosystem resilience study demonstrated that the current recovery of NPP primarily stems from the restoration of grassland ecosystems. Overall, while the potential for NPP recovery in co-seismic landslide areas is optimistic, it will require a considerable amount of time to return to the pre-earthquake state. Full article

Dashiling Gully, located in Miyun District, Beijing, exhibits a high susceptibility to debris flow due to its unique geological and topographical characteristics. The area is characterized by well-developed rock joints and fissures, intense weathering, a steep gradient, and a constricted gully morphology. These factors contribute to the accumulation of surface water and loose sediment, significantly increasing the risk of debris flow events. Following a comprehensive field geological investigation of Dashiling Gully, key parameters for simulation were obtained, including fluid weight, volume concentration, and rainfall. The formation and development conditions of potential mudslides were analyzed, and numerical simulations were conducted using FLO-2D software (version 2009) to assess scenarios with rainfall probabilities of 1 in 30, 50, and 100 years. The simulations accurately reconstructed the movement velocity, deposition depth, and other critical movement characteristics of mudslides under each rainfall scenario. Using ArcGIS, pre- and post-treatment hazard zoning maps were generated for Dashiling Gully. Furthermore, the efficacy of implementing a retaining wall as a mitigation measure was evaluated through additional numerical simulations. The results indicated that mudslide velocities ranged from 0 to 3 m/s, with deposition depths primarily between 0 and 3 m. The maximum recorded velocity reached 3.5 m/s, corresponding to a peak deposition depth of 4.31 m. Following the implementation of the retaining wall, the maximum deposition depth significantly decreased to 1.9 m, and high-risk zones were eliminated, demonstrating the intervention’s effectiveness. This study provides a rigorous evaluation of mudslide movement characteristics and the impact of mitigation measures within Dashiling Gully. The findings offer valuable insights and serve as a reference for forecasting and mitigating similar mudslide events triggered by heavy rainfall in gully mudslides. Full article

Under the background of climate warming in the Qinghai-Tibetan Plateau (QTP), frequent freeze–thaw cycling (FTC) brings about great geological disasters such as subgrade failure, landslides, and mudslides, which is closely related to the strength reduction caused by the structural damage of soils. In this study, to explore the association between macro shear strength and microstructure evolution of soils subjected to FTC, the red clay distributed widely in the QTP was chosen and used to conduct a series of triaxial shear and nuclear magnetic resonance (NMR) tests in the range of 1 to 7 FTCs. Triaxial shear test results reveal that the shear strength reduction of specimens mainly occurs within five FTCs, and the trend of peak deviator stress with increasing FTCs can be described in three stages: rapid descent (FTCs less than three), slow descent (FTCs between three and five), and stabilization (FTCs greater than five). NMR tests show that the T₂ spectrum curves exhibit a distinct bimodal distribution characteristic, corresponding to macropores and micropores. Part of the micropores gradually develop into macropores with increasing FTCs, especially within five FTCs. The increase in macropores proportion leads to a loose soil structure, which is consistent with the deterioration of the shear strength of specimens. Finally, based on the experimental results and classical Mohr–Coulomb theory, a new shear strength model with structural damage for red clay has been proposed by introducing a damage factor expressed by T₂ spectral area. Full article

Addressing the limitations, including low automation, slow recognition speed, and limited universality, of current mudslide disaster detection techniques in remote sensing imagery, this study employs deep learning methods for enhanced mudslide disaster detection. This study evaluated six object detection models: YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and YOLOX, conducting experiments on remote sensing image data in the study area. Utilizing transfer learning, mudslide remote sensing images were fed into these six models under identical experimental conditions for training. The experimental results demonstrate that YOLOX-Nano’s comprehensive performance surpasses that of the other models. Consequently, this study introduces an enhanced model based on YOLOX-Nano (RS-YOLOX-Nano), aimed at further improving the model’s generalization capabilities and detection performance in remote sensing imagery. The enhanced model achieves a mean average precision ($mAP$) value of 86.04%, a 3.53% increase over the original model, and boasts a precision rate of 89.61%. Compared to the conventional YOLOX-Nano algorithm, the enhanced model demonstrates superior efficacy in detecting mudflow targets within remote sensing imagery. Full article

Rainfall is the main cause of erosion damage in loose slope deposits. During rainfall infiltration, fine particles in the soil mass will move with water infiltration, thus changing the localized particle distribution of the soil mass, which, in turn, causes changes in the pore water pressure and volumetric water content within the slope and ultimately affects slope stability. In order to develop advanced soil and water conservation programs to prevent slope damage, it is crucial to understand and accurately reproduce the particle migration and aggregation characteristics of soils under different rainfall conditions. Therefore, this paper systematically investigates the soil particle migration characteristics of the soil body under rainfall conditions by simulating the internal erosion of the lateritic soil slope body under rainfall conditions via slope internal erosion simulation experiments and experimentally analyzing the migration and aggregation of fine particles in the slope body, as well as the changed rules regarding pore water pressure and volumetric water content at different locations of the slope body with rainfall. The results of this study show that (1) with the infiltration of rainfall, the fine particles in the slope body mainly infiltrate in the vertical direction in an early stage of rainfall; in a later stage, there is vertical downward and down-slope seepage. Therefore, fine particles always gather at the toe of the slope, which leads to relatively high water content and pore water pressure at the toe of the slope, and thus, the slope is always damaged from the toe of the slope. (2) Inside the slope, the fine particles always gather at the smallest pore diameter. With the enhancement of hydrodynamic force, they will be lost again, which leads to a sudden decrease in the local volumetric water content of the slope, and the pore space increases. Then, it is filled with seepage water, which makes the pore water pressure fluctuate or increase. (3) Based on the particle distribution parameter, the present study produced a distribution map of the fine particle content of the slope body under different rainfall intensities and established a model of the dynamic change of fine particles, which improves the understanding of the effect of the change in the fine particle composition of the slope body on the water content and the pore water pressure and may be helpful for the assessment of the initiation of the mudslides. Full article

Flash floods are a significant threat to arid and semi-arid regions, causing considerable loss of life and damage, including roads, bridges, check dams and dikes, reservoir filling, and mudslides in populated areas as well as agricultural fields. Flood risk is a complex process linked to numerous morphological, pedological, geological, anthropic, and climatic factors. In arid environments such as where Bayech basin is located in southwestern Tunisia, the hydrometric data are insufficient due to the absence of measuring points. Using the hybrid fuzzy Analytical Hierarchy Process (F-AHP) and the frequency ratio statistical methods, this study aims to map flooding risks in an ungauged basin that is extremely prone to flooding. Data related to soil texture, slope, land use, altitude, rainfall, drainage density, and distance from the river were used in the risk analysis. The obtained flood risk maps from both F-AHP and FR models were validated on the basis of the Receiver Operating Characteristic (ROC), the Area Under the Curve (AUC), and the inventory map. Results revealed that areas of high and very high susceptibility to flooding are mainly located in the downstream part of the basin, where the town of Gafsa is located. Other upstream sites are also at risk. In this basin, slope is predominantly behind runoff accumulation, whereas soil type plays a major role in amplifying waterproofing and therefore overflow. The results derived from both methods clearly demonstrate a viable and efficient assessment in flood-prone areas. The F-AHP and FR methods have ROC values of 95% and 97%, respectively. Considering these results in the decision-making process, these outputs would enable the implementation of the necessary measures to mitigate flood risk impacts ensure sustainable development along with an effective management in Tunisian arid environments, for the well-being of local communities at risk. Full article