Search Results (3,024)

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately −16 mm/a over the 22 km section of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 °C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze–thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone. Full article

The spatial and temporal distribution of heavy rainfall across the Taihang Mountains exhibits significant variation. Due to the region’s unstable geological conditions, frequent heavy rainfall events can lead to secondary disasters such as landslides, debris flows, and floods, thus intensifying both the frequency and severity of extreme events. Understanding the spatiotemporal evolution of heavy rainfall and its response to atmospheric circulation patterns is crucial for effective disaster prevention and mitigation. This study utilized daily precipitation data from 13 meteorological stations in the Taihang Mountains spanning from 1973 to 2022, employing Rotated Empirical Orthogonal Function (REOF), the Mann–Kendall Trend Test, and Continuous Wavelet Transform (CWT) to examine the spatiotemporal characteristics of heavy rainfall and its relationship with large-scale atmospheric circulation patterns. The results reveal that: (1) Heavy rainfall in the Taihang Mountains can be categorized into six distinct regions, each demonstrating significant spatial heterogeneity. Region I, situated in the transition zone between the plains and mountains, experiences increased rainfall due to orographic lifting, while Region IV, located in the southeast, receives the highest rainfall, driven primarily by monsoon lifting. Conversely, Regions III and VI receive comparatively less precipitation, with Region VI, located in the northern hilly area, experiencing the lowest rainfall. (2) Over the past 50 years, all regions have experienced an upward trend in heavy rainfall, with Region II showing a notable increase at a rate of 14.4 mm per decade, a trend closely linked to the intensification of the hydrological cycle driven by global warming. (3) The CWT results reveal significant 2–3-year periodic fluctuations in rainfall across all regions, aligning with the quasi-biennial oscillation (QBO) characteristic of the East Asian summer monsoon, offering valuable insights for future climate predictions. (4) Correlation and wavelet coherence analyses indicate that rainfall in Regions II, III, and IV is positively correlated with the Southern Oscillation Index (SOI) and the Pacific Warm Pool (PWP), while showing a negative correlation with the Pacific Decadal Oscillation (PDO). Rainfall in Region I is negatively correlated with the Indian Ocean Dipole (IOD). These climatic factors exhibit a lag effect on rainfall patterns. Incorporating these climatic factors into future rainfall prediction models is expected to enhance forecast accuracy. This study integrates REOF analysis with large-scale circulation patterns to uncover the complex spatiotemporal relationships between heavy rainfall and climatic drivers, offering new insights into improving heavy rainfall event forecasting in the Taihang Mountains. The complex topography of the Taihang Mountains, combined with unstable geological conditions, leads to uneven spatial distribution of heavy rainfall, which can easily trigger secondary disasters such as landslides, debris flows, and floods. This, in turn, further increases the frequency and severity of extreme events. Full article

A practical analysis of the spectrograms of the seismic data generated by gravitational mass movements (GMMs), such as snow avalanches, landslides, lahars, and debris flows recorded on one sensor, is presented. The seismic signal produced by these movements is analyzed in terms of the shape of the initial section of the spectrogram, which corresponds to the start of the movement of the gravitational mass. The shape of the envelope of the spectrogram is a consequence of the progressive reception of high-frequency energy in the signal as the gravitational mass (GM) approaches the sensor because of the attenuation properties of the seismic waves in the ground. An exponential law was used to fit this envelope of the onset signal. The proposed methodology allows us to obtain the propagation characteristics of different types of GMM. The analysis of the adjusted parameters for different types of GMM allows us to assert that differences of one order of magnitude exist in the values of these parameters depending on the type of event. In addition, differences in the values of the exponent were obtained between the events of each type of the analyzed GMM. We present a template of different curves for each type of GMM with the corresponding parameter values that can help professionals characterize a GMM with only one seismic record (one seismic sensor) whenever the mass movement approaches the recording sensor or passes over it. Full article

Mass movements can be caused by factors from different categories, such as geological factors and climate change. From a geological point of view, the soil profile and the geotechnical properties of the materials are crucial in influencing slope instability. From a climate change perspective, rainfall intensity is one of the main triggers of mass movements. Studies related to rainfall infiltration focus on saturated slope zones; therefore, areas of slope stability with infiltration in the unsaturated zone present large gaps. The Brazilian government environmental diagnostics company, the Mineral Resources Research Company (CPRM), identified the municipality of Areia/PB as a danger zone. The region has landslides that occur mostly during the rainy season. Such events lead to the presumption that rainwater infiltration is responsible for the failure of the municipality’s slopes. Thus, the studies proposed in this research aim to determine the influence of precipitation on the stability of the slopes present in the region. The results show that antecedent precipitation has a greater influence on stability, indicating that daily precipitation alone cannot be used as a determinant for landslides. It was concluded that the role of precipitation in slope stability will vary for different locations, with varying surface conditions, variable tropical rainfall, or different microclimatic conditions. Full article

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901. Full article

Landslides are common geological hazards worldwide, posing significant threats to both the environment and human lives. The preparation of a landslides susceptibility map is a major method to address the challenge related to sustainability. The study area, Nyingchi, is located in the southeastern region of the Qinghai-Tibet plateau, characterized by diverse terrain and complex geological formations. In this study, CNN was used to extract high-order features from the influencing factors, while BiLSTM was utilized to mine the historical data. Additionally, the attention mechanism was added to adjust the model weights dynamically. We constructed a hybrid CNN–BiLSTM-AM model to assess landslide susceptibility. A spatial database of 949 landslides was established using remote sensing images and field surveys. The effects of various feature selection methods were analyzed, and model performance was compared to that of six advanced models. The results show that the proposed model achieved a high prediction accuracy of 90.12% and exhibits strong generalization capabilities over large areas. It should be noted, however, that the influence of feature selection methods on model performance remains uncertain under complex conditions and is affected by multiple mechanisms. Full article

On 1–3 May 2023, severe hydro-meteorological events occurred in the Italian Emilia-Romagna region. Such events caused extensive flooding, landslides, isolation of many areas, evacuation of many families, and severe damage to infrastructure, agriculture, buildings, and essential services. Several municipalities were affected, thousands of civilians had to be evacuated, and losses of life occurred. The consequences beyond the recorded immediate impacts on infrastructure and life were impressive, and extended to the regional economy, specifically in the Fruit Valley, where, in addition to immediate yield losses, long-term damage to orchard production is expected due to persistent flooding. The civil and cultural building heritage has also been heavily affected, both in the countryside and in inhabited centers. Some of the damage, direct and indirect, caused by flooding on buildings will also see an evolution in the medium- to long-term that needs to be addressed. This paper analyzes the manifold aspects of such an atmospheric phenomenon and its impacts to understand the potential increasing occurrence of similar events in the climate change context. Full article

This study presents the geological and geomorphological characterization of the Pie de la Cuesta landslide, a large (>60 ha) slow-moving (up 4.5 m/month) landslide in Southern Peru. The landslide has been active since 1975 and underwent a significant re-activation in 2016; the mass movement has caused the loss of property and agricultural land and it is currently moving, causing further damage to property and land. We use a combination of historical aerial photographs, satellite images and field work to characterize the landslide’s geology and geomorphology. The landslide is affecting the slope of the Vitor Valley, constituted by a coarsening upward sedimentary sequence transitioning from layers of mudstone and gypsum at the base, to sandstone and conglomerate at the top with a significant ignimbrite layer interbedded within conglomerates near the top of the sequence. The landslide is triggered by an irrigation system that provides up to 10 L/s of water infiltrating the landslide mass. This water forms two groundwater levels at lithological transitions between conglomerates and mudstones, defining the main failure planes. The landslide is characterized by three main structural domains defined by extension, translation and compression deformation regimes. The extensional zone, near the top of the slope, is defined by a main horst–graben structure that transitions into the translation zone defined by toppling and disaggregating blocks that eventually become earth flows that characterize the compressional zone at the front of the landslides, defined by thrusting structures covering the agricultural land at the valley floor. The deformation rates range from 8 cm/month at the top of the slope to 4.5 m/month within the earth flows. As of May 2023, 22.7 ha of potential agricultural land has been buried. Full article

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake’s central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts. Full article

Deep-Seated Gravitational Slope Deformations (DSGSDs) are common in many geological environments, and due to their common limited displacement rate, they can remain unrecognized for a long time. Among the most significant events in Sicily is the Mt. San Calogero DSGSD. To contribute to a better understanding of its characteristics, including the geologic setting promoting its development, ongoing kinematics, and mechanism, a specific analysis was completed. In this paper, the results of this analysis, based on a three-folded strategy, are provided and interpreted in the context of DSGSD predisposing conditions and controlling factors. Especially, field observations associated to visual interpretation of aerial imagery were used for the identification and mapping of main geological features and landforms, high-resolution X-Band DInSAR data enabled researchers to fully characterize the deformational behavior of the slope, while a reduced complexity slope stability analysis allowed them to reconstruct the deep geometry of the DSGSD. Results from the analysis indicate that the DSGSD of Mt. San Calogero is composed of three blocks corresponding to fault-bounded tectonic elements and characterized by a specific kinematics and sensitivity to external forcing (i.e., rainfall), multiple landslides are associated to the DSGSD in the area and the deep geometry of the DSGSD is concave upward and resemble the characteristics of a rotational slide. The interpretation of the results suggests that the formation and the deformation of the Mt. San Calogero DSGSD are linked with the local and regional fault systems related to the Sicilian orogen, while shallow landslides are triggered, in clayey terrains, mostly by rainfalls. In addition, the integrated approach reveals that active tectonics and rainfalls in the San Calogero massive relief are the main driving forces of its different deformation behavior. Full article

Mining causes damage to the soil and rock mass, while rainfall has a pivotal impact on the mining slope stability, even leading to geological hazards such as landslides. Therefore, the study evaluated the mine landslide stability and determined the effectiveness of the treatment measures under the impact of pore water pressure changes caused by rainfall, taking the Kong Mountain landslide in Nanjing, Jiangsu Province, China, as the research object. The geological conditions and deformation characteristics were clarified, and the failure mechanism and influencing factors were analyzed. Also, the landslide stability was comprehensively evaluated and calculated utilizing the finite element-improved limit equilibrium method and FLAC 3D 6.0, which simulated the distribution of pore water pressure, displacement, etc., to analyze the influence of rainfall conditions and reinforcement effects. The results indicated the following: (1) Rainfall is the key influencing factor of the landslide stability, which caused the pore water pressure changes and the loosening of the soil due to the strong permeability; (2) The distribution of the pore water pressure and plastic zone showed that, during the rainfall process, a large area of transient saturation zone appeared at the leading edge, affecting the stability of the whole landslide and led to the further deformation; (3) After the application of treatment measures (anti-sliding piles and anchor cables), the landslide stability increased under both natural and rainfall conditions (from 1.02 and 0.94 to 1.38 and 1.31, respectively), along with a reduction in displacement, plastic zones, etc. The Kong Mountain landslide, with the implemented treatment measures, is in good stability, which is in line with the evaluation and calculation results. The study provides certain contributions to the stability evaluation and treatment selection of similar engineering under rainfall infiltration. Full article

A landslide susceptibility assessment is one of the critical steps in planning for landslide disaster prevention. Advanced machine learning methods can be used as data-driven approaches for landslide susceptibility zonation with several landslide conditioning factors. Despite there being a number of studies on landslide susceptibility assessment, the literature is limited in several contexts, such as parameter optimization, an examination of the factors in detail, and study area. This study addresses these lacks in the literature and aims to develop a landslide susceptibility map of Kentucky, US. Four machine learning methods, namely artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and stochastic gradient boosting (SGB), were used to train the dataset comprising sixteen landslide conditioning factors after pre-processing the data in terms of data encoding, data scaling, and dimension reduction. The hyperparameters of the machine learning methods were optimized using a state-of-the-art artificial bee colony (ABC) algorithm. The permutation importance and Shapley additive explanations (SHAP) methods were employed to reduce the dimension of the dataset and examine the contributions of each landslide conditioning factor to the output variable, respectively. The findings show that the ABC-SGB hybrid model achieved the highest prediction performance. The SHAP summary plot developed using the ABC-SGB model shows that intense precipitation, distance to faults, and slope were the most significant factors affecting landslide susceptibility. The SHAP analysis further underlines that increases in intense precipitation, distance to faults, and slope are associated with an increase in the probability of landslide incidents. The findings attained in this study can be used by decision makers to develop the most effective resource allocation plan for preventing landslides and minimizing related damages. Full article

Earthquake-induced landslides (EQILs) represent a serious secondary disaster of earthquakes, and conducting an effective assessment of earthquake-induced landslide susceptibility (ELSA) post-earthquake is helpful in reducing risk. In light of the diverse demands for ELSA across different time periods following an earthquake and the growing availability of data, this paper proposes using remote sensing data to dynamically update the ELSA model. By studying the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023, rapid assessment results were derived from 12 pre-trained ELSA models combined with the spatial distribution of historical earthquake-related landslides immediately after the earthquake for early warning. Throughout the entire emergency response stage, the ELSA model was dynamically updated by integrating the EQILs points interpreted from remote sensing images as new training data to enhance assessment accuracy. After the emergency phase, the remote sensing interpretation results were compiled to create the new EQILs inventory. A high landslide potential area was identified using a re-trained model based on the updated inventory, offering a valuable reference for risk management during the recovery phase. The study highlights the importance of integrating remote sensing into ELSA model updates and recommends utilizing time-dependent remote sensing data for sampling to enhance the effectiveness of ELSA. Full article

Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the integration of topological, geometrical, and contextual information to enhance the precision of landslide detection. Topology, a fundamental branch of mathematics, explores the properties of space that are preserved under continuous transformations and focuses on the qualitative aspects of space, studying features like connectivity and exitance of loops/holes. We employed persistent homology (PH) to derive candidate polygons and applied three distinct strategies for landslide detection: without any filters, with geometrical and contextual filters, and a combination of topological with geometrical and contextual filters. Our method was rigorously tested across five different study areas. The experimental results revealed that geometrical and contextual filters significantly improved detection accuracy, with the highest F1 scores achieved when employing these filters on candidate polygons derived from PH. Contrary to our initial hypothesis, the addition of topological information to the detection process did not yield a notable increase in accuracy, suggesting that the initial topological features extracted through PH suffices for accurate landslide characterization. This study advances the field of geospatial object detection by demonstrating the effectiveness of combining geometrical and contextual information and provides a robust framework for accurately mapping landslide susceptibility. 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