Search Results (275)

In this study, an ANN-derived innovative model was developed for estimating the failure soil depths of rainfall-induced shallow landslide events, named the SM_EFD_LS model. The proposed SM_EFD_LS model was created using the modified ANN model via the genetic algorithm calibration approach (GA-SA) with multiple transfer functions (MTFs) (ANN_GA-SA_MTF) with a significant number of failure soil depths and corresponding rainfall factors. Ten shallow landslide-susceptible spots in the Jhuokou watershed in southern Taiwan were selected as the study area. The associated 1000 simulations of rainfall-induced shallow landslide events were used in the model’s development and validation. The model validation results indicate that the validated failure soil depths are mainly located within the resulting 60% confidence intervals from the proposed SM_EFD_LS model. Moreover, the estimated failure depths resemble the validated ones, with acceptable averages of the absolute error (RMSE) and relative error (MRE) (11 cm and 0.06) and a high model reliability index of 1.2. In the future, the resulting probabilistic-based failure soil depths obtained using the proposed SM_EFD_LS model could be introduced with the desired reliability needed for early landslide warning and prevention systems. Full article

The stability of slopes is influenced by seasonal variations in thermal, hydrological, and mechanical processes. This study investigates the role of snowmelt in triggering shallow landslides through controlled laboratory experiments simulating winter, spring, and summer conditions. Snowpack dynamics and water movement were analyzed to understand filtration, infiltration, and runoff mechanisms. The results show that during winter, snow acts as a protective layer, slowing infiltration through its insulating and loading effects. In spring, rising temperatures melt snow, increasing water infiltration and filtration, accelerating soil saturation, and triggering slope failures. Summer rainfall-induced landslides exhibit distinct mechanisms, driven by progressive saturation. The transition from winter to spring highlights a critical phase where snowmelt interacts with warmer soils, intensifying slope instability risks. Numerical simulations using HYDRUS 1D validated the experimental findings, demonstrating its utility in modeling infiltration under varying thermal gradients. This study underscores the importance of incorporating snowmelt dynamics into landslide risk assessments and early warning systems, particularly as climate change accelerates snowmelt cycles in mountainous regions. These findings provide essential insights into seasonal variations in collapse mechanisms, emphasizing the need for further research to address the increasing impact of snowmelt in shallow landslides. Full article

Accurate prediction of landslide displacement is crucial for disaster prevention and mitigation. This study proposes an STL-TCN-Transformer model that combines time series decomposition with deep learning to predict cumulative displacement. Using monitoring data from the Baishuihe landslide, the displacement sequence was decomposed into trend, periodic, and residual components using the STL method. The trend component, determined by geotechnical properties, was predicted using a univariate TCN-Transformer, while the periodic and residual components, influenced by rainfall and reservoir water levels, were analyzed for nonlinear correlations using the Spearman method and predicted with a multivariate TCN-Transformer. The proposed model achieved superior performance, with R² of 0.993, RMSE of 7.82 mm, and MAE of 5.82 mm, significantly outperforming EMD-LSTM, EEMD-RNN, and VMD-BiLSTM models in all metrics. These findings demonstrate the ability of the STL-TCN-Transformer to effectively capture the dynamics of landslide displacement, offering a reliable approach for landslide monitoring and early warning systems. Full article

In light of the increasing frequency of extreme rainfall events, there has been a concomitant rise in landslides triggered by such precipitation. Despite the extensive research conducted on rainfall-induced landslides, the practical implementation of these findings is constrained by geological and environmental factors. Notably, there is a paucity of research on rainfall-induced shallow landslides in Hubei Province, China. Therefore, this study analyzes the fundamental characteristics and rainfall characteristics of landslides induced by multiple rounds of extreme rainfall in Qichun County in June and July 2016. The study explores the influence of five variables—namely, altitude, slope, slope aspect, stratum lithology, and rainfall—on landslides. The study uses numerical analysis to reveal the initiation mechanism of landslides. The research conclusions are as follows: The landslides within the study area are closely related to its natural topography, stratum lithology, and human activities. The majority of landslides are triggered by short-term extreme rainfall, while a smaller number are related to long-term continuous rainfall. The formation mechanism of landslides is primarily driven by dynamic water seepage, and the destruction process often lags behind the rainfall process. The conclusions can provide theoretical guidance for risk prevention and early warning of rainfall-induced landslides in the region. Full article

The Jiroft Dam, situated in Kerman province, Iran, serves as a crucial infrastructure for water management, flood control, and agricultural development in the region. However, the surrounding mountainous terrain presents considerable geotechnical challenges that threaten the stability of access roads and other essential infrastructure. This study is based on comprehensive field surveys and mapping, which have revealed significant ground displacements and evidence of slope instabilities in the area. The investigation identifies key factors, including soil composition, rock formations, groundwater flow, and seismic activity, that contribute to these shifts in the terrain. To ensure the accuracy of the elevation data, the study employed Monte Carlo simulation techniques to analyze the statistical distribution of the collected survey data. By simulating various possible outcomes, this study enhanced the precision of the elevation models, allowing for better identification of critical instability zones. Additionally, the Overall Equipment Effectiveness (OEE) was utilized to evaluate the effectiveness of the current monitoring equipment and infrastructure, providing a clearer understanding of operational efficiency and areas for improvement. The findings of this study highlight the immediate need for effective risk management strategies to mitigate the potential hazards of landslides and infrastructure failure. Addressing these challenges is essential to ensure the long-term sustainability of the region’s infrastructure. In response to these observations, this research proposes practical engineering solutions such as slope stabilization techniques and improved drainage systems to address the identified instabilities. Furthermore, this study underscores the necessity of the continuous monitoring and the implementation of early warning systems to detect further ground movements and mitigate associated risks.In addition to technical interventions, this research emphasizes the importance of integrating local knowledge and expertise into the risk management process. Full article

Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into “essentially stable”, “marginally stable”, “unstable”, and “rapidly deforming” categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters. Full article

The Heifangtai Loess terrace in northwest China is frequently affected by landslides due to hydrological factors, establishing it as a significant research area for loess landslides. Advanced time-series InSAR technology facilitates the retrieval of surface deformation information, thereby aiding in the monitoring of landslide deformation status. However, existing methods that analyze deformation patterns do not fully exploit the displacement time series derived from InSAR, which hampers the exploration of potentially coexisting deformation patterns within the area. This study integrates InSAR with time-series clustering methods to reveal the surface deformation patterns and their spatial distribution characteristics in Heifangtai. Initially, utilizing the Sentinel-1 ascending and descending SAR data stack from January 2020 to June 2023, we optimize the interferometric phase based on distributed scatterer characteristics to reduce noise levels and obtain higher spatial density of measurement points. Subsequently, by combining the differential interferometric datasets from both ascending and descending orbits, the multidimensional small baseline subsets technique is employed to calculate the two-dimensional deformation time series. Finally, time-series clustering methods are utilized to extract the deformation patterns present and their spatial distribution from all measurement point time series. The results indicate that the deformation of the Heifangtai is primarily distributed around the surrounding area of the platform, with subsidence deformation being more intense than horizontal deformation. The entire terrace exhibits five deformation patterns: eastward subsidence, westward subsidence, vertical subsidence, westward movement, and eastward movement. The spatial distribution of these patterns suggests that the areas beneath the platform, namely Yanguoxia Town and Dangchuan Village, may be more susceptible to landslide threats in the future. Furthermore, wavelet analysis reveals the response relationship between rainfall and various deformation patterns, further enhancing the interpretability of these patterns. These findings hold significant implications for subsequent landslide monitoring, early warning, and risk prevention. Full article

This study systematically analyzed research trends and hot issues in the field of geological hazard prediction using bibliometric analysis methods. A total of 12,123 related articles published from 1976 to 2023 were retrieved from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases. Co-occurrence analysis and burst detection were conducted on the literature using the VOSviewer and CiteSpace tools to identify the research trends in geological hazard monitoring technologies. The results reveal that “data fusion”, “landslide identification”, “deep learning”, and “risk early warning” are currently the main research hot spots. Additionally, the combined application of Global Navigation Satellite System (GNSS) and Real-Time Kinematic (RTK) technologies, as well as GNSS and Long Short-Term Memory (LSTM) models, were identified as important directions for future research. The bibliometric perspective offers a systematic theoretical framework and technical guidance for future research, thereby facilitating the sustainable advancement of safety, security, and disaster management. Full article

The scarcity of landslide samples poses a critical challenge, impeding the broad application of machine learning techniques in landslide susceptibility assessment (LSA). To address this issue, this study introduces a novel approach leveraging a deep convolutional generative adversarial network (DCGAN) for data augmentation aimed at enhancing the efficacy of various machine learning methods in LSA, including support vector machines (SVMs), convolutional neural networks (CNNs), and residual neural networks (ResNets). Experimental results present substantial enhancements across all three models, with accuracy improved by 2.18%, 2.57%, and 5.28%, respectively. In-depth validation based on large landslide image data demonstrates the superiority of the DCGAN-ResNet, achieving a remarkable landslide prediction accuracy of 91.31%. Consequently, the generation of supplementary samples via the DCGAN is an effective strategy for enhancing the performance of machine learning models in LSA, underscoring the promise of this methodology in advancing early landslide warning systems in western Sichuan. Full article

In the current work, authors reviewed the latest research results in landslide susceptibility mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of collected publications, the review was classified into four sections based on their complexity: single-model approaches, enhanced models with optimization, ensemble models, and hybrid models. Each category offers distinct advantages and is suited to specific geographic and data conditions, enabling the selection of an optimal model type based on the complexity and requirements of the mapping task. Among models, random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and multilayer perception (MLP) are used as the baseline to compare any new model introduced to develop LSM. Moreover, compared to previous review works, the number of LSM conditioning factors used in AI models are significantly increased, up to 122 factors. Their relation to the AI models is illustrated using Sankey diagram, while a radar chart is used to further visualize the dataset size per reviewed work for comparative purposes. In the main part of the current review work, the main findings are summarized into a table form, where the reader can find the overall relations between landslide conditioning factors, landslide dataset size, applied AI models, and their accuracy on predicting LSM for selected geographical locations. In terms of the regions, Asia is leading in the application of AI models to generate LSM, and in such regions with dense populations falling into higher landslide risk categories, there are more ongoing research activities, using modern AI methods. This trend underscores the increased use of AI in disaster management, with implications for improving practical applications, such as early warning systems and informing policy decisions aimed at risk reduction in vulnerable areas. Full article

Understanding the factors that contribute to slope failures, such as soil saturation, is essential for mitigating rainfall-induced landslides. Cost-effective capacitive soil moisture sensors have the potential to be widely implemented across multiple sites for landslide early warning systems. However, these sensors need to be calibrated for specific applications to ensure high accuracy in readings. In this study, a soil-specific calibration was performed in a laboratory setting to integrate the soil moisture sensor with an automatic monitoring system using the Internet of Things (IoT). This research aims to evaluate a low-cost soil moisture sensor (SKU:SEN0193) and develop calibration equations for the purpose of slope model experiment under artificial rainfall condition using silica sand. The results indicate that a polynomial function is the best fit, with a coefficient of determination (R²) ranging from 0.918 to 0.983 and a root mean square error (RMSE) ranging from 1.171 to 2.488. The calibration equation was validated through slope model experiments, with soil samples taken from the models after the experiment finished. Overall, the moisture content readings from the sensors showed approximately a 12% deviation from the actual moisture content. The findings suggest that the cost-effective capacitive soil moisture sensor has the potential to be used for the development of landslide early warning system. Full article

Landslides are common natural disasters in mountainous regions, exerting considerable influence on socioeconomic development and city construction. Landslides occur and develop rapidly, often posing a significant threat to the safety of individuals and their property. Consequently, the mapping of areas susceptible to landslides and the simulation of the development of such events are crucial for the early warning and forecasting of regional landslide occurrences, as well as for the management of associated risks. In this study, a landslide susceptibility (LS) model was developed using an ensemble machine learning (ML) approach which integrates geological and geomorphological data, hydrological data, and remote sensing data. A total of nine factors (e.g., surface deformation rates (SDF), slope, and aspect) were used to assess the susceptibility of the study area to landslides and a grading of the LS in the study area was obtained. The proposed model demonstrates high accuracy and good applicability for LS. Additionally, a simulation of the landslide process and velocity was constructed based on the principles of landslide movement and the rule-based discrete grid model. Compared with actual unmanned aerial vehicle (UAV) imagery, this simulation model has a Sørensen coefficient (SC) of 0.878, a kappa coefficient of 0.891, and a total accuracy of 94.12%. The evaluation results indicate that the model aligns well with the spatial and temporal development characteristics of landslides, thereby providing a valuable reference basis for monitoring and early warning of landslide events. Full article

Snow avalanche is one of the major natural hazards in the mountain region, yet it has received less attention compared to other mountain hazards, such as landslides, floods, and droughts. After a comprehensive overview of snow avalanche hazards in Tibet area, the spatial distribution and main driving factors of snow avalanche hazards in the high mountain region in Tibet were presented in the study first. Snow avalanche-prone areas in Tibet were then mapped based on the snow cover distribution and DEM data and were validated against in situ observations. Results show that there are the highest frequencies of avalanche occurrences in the southeastern Nyainqentanglha Mountains and the southern slope of the Himalayas. In the interior of plateau, avalanche development is constrained due to less precipitation and much flatter terrain. The perennially snow avalanche-prone areas in Tibet account for 1.6% of the total area of the plateau, while it reaches 2.9% and 4.9% of the total area of Tibet in winter and spring, respectively. Snow avalanche hazards and fatalities appear to be increasing trends under global climate warming due to more human activities at higher altitudes. In addition to the continuous implementation of engineering prevention and control measures in pivotal regions in southeastern Tibet, such as in the Sichuan–Tibet highway and railway sections, enhancing monitoring, early warning, and forecasting services are crucial to prevent and mitigate avalanche hazards in the Tibetan high mountain regions, which has significant implications for other global high mountain areas. Full article

Landslides are among the most serious and frequent environmental disasters, involving the fall of large masses of rock and soil that can significantly impact human structures and inhabited areas. Anticipating these events is crucial to reduce risks through real-time monitoring of areas at risk during extreme weather events, such as heavy rains, allowing for early warnings. This study aims to develop a methodology to enhance the prediction of landslide susceptibility, creating a more reliable system for early identification of risk areas. Our project involves creating a model capable of quickly predicting the susceptibility index of specific areas in response to extreme weather events. We represent the terrain using cellular automata and implement a random forest model to analyze and learn from weather patterns. Providing data with high spatial accuracy is vital to identify vulnerable areas and implement preventive measures. The proposed method offers an early warning mechanism by comparing the predicted susceptibility index with the current one, allowing for the issuance of alarms for the entire observed area. This early warning mechanism can be integrated into existing emergency protocols to improve the response to natural disasters. We applied this method to the area of Prunella, a small village in the municipality of Melito di Porto Salvo, known for numerous historical landslides. This approach provides an early warning mechanism, allowing for alarms to be issued for the entire observed area, and it can be integrated into existing emergency protocols to enhance disaster response. Full article

The present work explores the use of geophysical surveys as valuable tools for the study and sustainable management of landslides, with a particular focus on Ecuador. As an Andean country, Ecuador’s geomorphology and geology are dominated by volcano-sedimentary materials and processes, which confers a high susceptibility to landslides. In the last few years, a number of landslide events (such as those at La Josefina, Alausí, and Chunchi) have given rise to disasters with significant material damage and loss of life. Climatic events, affected by climate change, earthquakes, and human activity, are the main landslide triggers. Geophysical surveys, like seismic refraction, electrical resistivity tomography (ERT), and ground-penetrating radar (GPR), are easy and low-cost techniques that provide valuable and critical subsurface data. They can help define the failure surface, delimit the mobilized materials, describe the internal structure, and identify the hydrological and geotechnical parameters that complement any direct survey (like boreholes and laboratory tests). As a result, they can be used in assessing landslide susceptibility and integrated into early warning systems, mapping, and zoning. Some case examples of large landslide events in Ecuador (historical and recent) are analyzed, showing how geophysical surveys can be a valuable tool to monitor landslides, mitigate their effects, and/or develop solutions. Combined or isolated geophysical techniques foster sustainable management, improve hazard characterization, help protect the most vulnerable regions, promote community awareness for greater safety and resilience against landslides, and support governmental actions and policies. Full article