Search Results (48)

This study focuses on assessing flash flood risks in Northeastern Thailand, particularly within the Lam Saphung, Phrom, and Chern River Basins, which are highly susceptible to flash floods and debris flows. Using the HEC-RAS hydraulic model integrated with GIS tools, the research analyzes historical and scenario-based flood events to evaluate the impact of land use changes and hydrological dynamics. The model was calibrated and validated with statistical metrics such as R² values ranging from 0.745 to 0.994 and NSE values between 0.653 and 0.893, indicating strong agreement with the observed data. This study also identified high-risk areas, with up to 5.49% and 5.50% increases in flood-prone areas in the Phrom and Chern River Basins, respectively, from 2006 to 2019. Key findings highlight the critical role of proactive risk management and targeted mitigation strategies in enhancing community resilience. The integration of advanced hydraulic modeling with detailed datasets enables precise flood hazard mapping, including flood depths exceeding 1.5 m in certain areas and high-risk zones covering up to 105.2 km² during severe flood events. These results provide actionable insights for emergency response and land use planning. This research significantly contributes to hydrological risk assessments by advancing modeling techniques and delivering practical recommendations for sustainable flood management. The outcomes are particularly relevant for stakeholders, including urban planners, emergency management officials, and policymakers, who aim to strengthen resilience in vulnerable regions. By addressing the complexities of flash flood risk assessments with robust quantitative evidence, this study not only enhances the understanding of flood dynamics, but also lays the groundwork for developing adaptive strategies to mitigate the adverse impacts of flash floods, safeguarding both communities and infrastructure in the region. Full article

It is widely accepted that land use and land cover (LULC) is an important conditioning factor for landslide occurrence, especially when considering the role of tree roots in stabilizing slopes and consolidating the soil. However, it is still difficult to assess the impacts of a specific LULC type on landslide distribution. The objective of the present study is to reveal the relationship between bamboo and landslide distribution at the regional scale. We aim to answer the following question: do the areas covered by bamboo have a higher susceptibility to landslides? Wenzhou City in SE China was taken as the study area, and a landslide inventory containing 1725 shallow landslides was constructed. The generalized additive model (GAM) was employed to assess the significance of LULC and nine additional factors, all of which were generated using the GIS platform. The frequency ratio (FR) method was used to analyze and compare the landslide density in each LULC category. Machine learning models were applied to perform landslide susceptibility mapping of the region. The results show that in the Wenzhou region, LULC is the second most important factor for landslide occurrences after the slope factor, whereas bamboo has a relatively higher FR value than most other LULC categories. The accuracies of the landslide susceptibility maps obtained from the random forest and XGBoost models were 79.6% and 85.3%, respectively. Moreover, 23.8% and 25.5% of the bamboos were distributed in very-high- and high-susceptibility-level areas. The incidents and density of landslides in bamboo areas were significantly higher than those with debris flow and rock collapses, indicating a promotional effect of bamboo on slope failure in the study area. This work will improve our understanding regarding the role of geological and ecological conditions that affect slope stability, which may provide useful guidance for land use planning and landslide risk assessment and mitigation at the regional scale. Full article

The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM considering different sample representation forms. The sample representation forms mainly include a single grid, multi-grid, and watershed unit, and the negative-sample acquisition strategies are based on support vector machine (SVM), spy technique, and isolation forest (IF) methods, respectively. These three strategies can assign a value to all the samples based on different assumptions, and reliable, negative samples can be generated from samples with values below a predefined threshold. Combining different sample representation forms with negative sample acquisition strategies, nine datasets were then involved in random forest (RF) modeling. The receiver operating characteristic (ROC) curves and related statistical results were used to evaluate the models. The results show that the strategy based on the spy technique is suitable for multiple datasets, while the IF-based strategy is well-adapted to the watershed unit datasets. This study can provide more options for improving the quality of datasets in DFSM, which can further improve the performance of machine learning models. Full article

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale. 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

A landslide is the movement of rocks, debris, and/or soils down a slope, which often includes falls, topples, slides, flows, and spreads. Landslides, a serious natural hazard to human and human activity, often occur in the coastal and mountainous areas in the United States. Although there are some studies that have explored the landslide probability, which is typically directly modeled by inputting potential environmental variables into statistical regression models, this study designed an alternative geospatial analysis and modeling approach. We first conducted statistical diagnostic tests to examine the significance of potential driving factors including landform, land use/land cover, landscape, and climate. In eastern Tennessee, USA, we first applied the t-test and chi-squared test to select the significant factors driving landslides, including slope, clay percentage in the soil, tree canopy density, and distance to roads, having a p-value of less than 0.05. We then incorporated the four identified significant factors as covariates into logistic regression to model the relationship between these factors and landslides. The fitted logistic model, with a high area under the ROC (AUC) score of 0.94, was then applied to predict landslides and make a regional landslide susceptibility map for eastern Tennessee. The landslide’s potential impacts on eastern Tennessee were also discussed, and implications for local governments and communities for current physical infrastructure protection and new infrastructure development were summarized. Full article

With the continuous increase in global climate change and human activities, the risk of sudden debris flow disasters is becoming increasingly severe. In order to effectively evaluate and zone the potential hazards of debris flows, this paper proposes a method for zoning the potential sudden hazards of debris flows based on deep neural networks. According to hazard identification, ten risk indicators of potential sudden debris flows are determined. The risk indicators of a potential sudden debris flow in each region were used as the input factors of a deep trust network (DBN) composed of a back propagation (BP) neural network and a restricted Boltzmann machine (RBM). The DBN is pre-trained using the contrast divergence method to obtain the optimal value of the parameter set of the DBN model, and a BP network is set at the last layer of the DBN for fine-tuning to make the network optimal. Using the DBN model with the best parameters, the risk probability of debris flows corresponding to each region is taken as an output. The risk grade is divided, the risk degree of potential sudden debris flow in each region is analyzed, and the potential sudden debris flow risk in each region is divided individually. The results show that this method can effectively complete the risk zoning of sudden debris flow. Moreover, the cumulative contribution of the indicators selected by this method is significant, and the correlation of indicators is not significant, which can play a role in the risk assessment of potential sudden debris flow. This study not only provides new ideas and methods for risk assessment of sudden debris flow disasters, but also fills a gap in the field of geological hazard susceptibility mapping. Full article

Landslide occurrence in Colombia is very frequent due to its geographical location in the Andean mountain range, with a very pronounced orography, a significant geological complexity and an outstanding climatic variability. More specifically, the study area around the Bogotá-Villavicencio road in the central sector of the Eastern Cordillera is one of the regions with the highest concentration of phenomena, which makes its study a priority. An inventory and detailed analysis of 2506 landslides has been carried out, in which five basic typologies have been differentiated: avalanches, debris flows, slides, earth flows and creeping areas. Debris avalanches and debris flows occur mainly in metamorphic materials (phyllites, schists and quartz-sandstones), areas with sparse vegetation, steep slopes and lower sections of hillslopes; meanwhile, slides, earth flows and creep occur in Cretaceous lutites, crop/grass lands, medium and low slopes and lower-middle sections of the hillslopes. Based on this analysis, landslide susceptibility models have been made for the different typologies and with different methods (matrix, discriminant analysis, random forest and neural networks) and input factors. The results are generally quite good, with average AUC-ROC values above 0.7–0.8, and the machine learning methods are the most appropriate, especially random forest, with a selected number of factors (between 6 and 8). The degree of fit (DF) usually shows relative errors lower than 5% and success higher than 90%. Finally, an integrated landslide susceptibility map (LSM) has been made for shallower and deeper types of movements. All the LSM show a clear zonation as a consequence of the geological control of the susceptibility. Full article

The Italian territory is subject to a high level of hydrogeological instability that periodically results in the loss of lives, buildings and productive activities. Therefore, the recognition of areas susceptible to hydrogeological instability is the basis for preparing countermeasures. In this context, landslide susceptibility in the mid-Adriatic slope was analyzed using a statistical method, the weight of evidence (WoE), which uses information from several independent sources to provide sufficient evidence to predict possible system developments. Only flows, slides, debris flows and mud flows were considered, with a total of 14,927 landslides obtained from the IFFI (Inventory of Franous Phenomena in Italy) database. Seven climatic–environmental factors were used for mapping landslide susceptibility in the study area: slope, aspect, extreme precipitation, normalized difference vegetation index (NDVI), CORINE land cover (CLC), and topographic wetness index (TWI). The introduction of these factors into the model resulted in rasters that allowed calculation by GIS-type software of a susceptibility map. The result was validated by the ROC curve method, using a group of landslides, equal to 20% of the total, not used in the modeling. The performance of the model, i.e., the ability to predict the presence or absence of a landslide movement correctly, was 0.75, indicating a moderately accurate model, which nevertheless appears innovative for two reasons: the first is that it analyzes an inhomogeneous area of more than 9000 km², which is very large compared to similar analyses, and the second reason is the causal factors used, which have high weights for some classes despite the heterogeneity of the area. This research has enabled the simultaneous introduction of unconventional factors for landslide susceptibility analysis, which, however, could be successfully used at larger scales in the future. Full article

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention. Full article

The impacts of climate change are becoming more widespread across the world, with hydro-meteorological extreme events on the rise, causing severe threats to nature and communities. Increasing trends in the frequency and intensity of floods and landslides have been projected by climate models. This necessitates the development of more effective measures such as nature-based solutions (NBS) which can complement grey infrastructures. Recent studies have identified knowledge gaps and limitations in existing research and tools that aid in spatial planning for the implementation of large-scale NBS and proposed new methodologies for the spatial allocation of large-scale NBS for flood risk reduction. This work presents a novel method for mapping the suitability of NBS addressing geo-hydrological hazards such as shallow landslides, debris flow, and rockfall, which are typically caused due to slope instability. This methodology incorporates landslide susceptibility mapping, and was used to create a toolbox ESRI ArcGIS environment to aid decision-makers in the planning and implementation of large-scale NBS. The spatial allocation toolbox was applied to the case study Portofino promontory, Liguria region, Italy, and 70% of the area was found to be highly susceptible to landslides. The produced suitability maps show that 41%, 33%, and 65% of the study area is suitable for the restoration of terraces, bio-engineering, and vegetative measures such as NBS for landslide risk reduction. Full article

In July 2015, more than 100 landslides caused by Cyclone Komen resulted in damage to approximately 1000 buildings in the mountainous region of Hakha City, Myanmar. This study aimed to identify potential landslide susceptibility for newly developed resettlement areas in Hakha City before and after urbanization. The study evaluated landslide susceptibility through statistical modeling and compared the level of susceptibility before and after urbanization in the region. The information value model was used to predict landslide susceptibility before and after urbanization, using 10 parameter maps as independent variables and 1 landslide inventory map as the dependent variable. Four landslide types were identified in the study area: shallow earth slide, deep slide, earth slump, and debris flow. Susceptibility analyses were conducted separately for each type to better recognize the different aspects of landslide susceptibility in planned urban areas. By comparing the results of the susceptibility index before and after urbanization, suitable urban areas with lower landslide susceptibility could be identified. The results showed that high-potential landslide susceptibility increased by 10%, 16%, and 5% after urbanization compared with before urbanization in three Town Plans, respectively. Therefore, Town Plan 3 is selected as the most suitable location for the resettlement area in terms of low risk of landslides. Full article

In the mapping and assessment of mountain hazard susceptibility using machine learning models, the selection of model parameters plays a critical role in the accuracy of predicting models. In this study, we present a novel approach for developing a prediction model based on random forest (RF) by incorporating ensembles of hyperparameter optimization. The performance of the RF model is enhanced by employing a Bayesian optimization (Bayes) method and a genetic algorithm (GA) and verified in the Wudu section of the Bailong River basin, China, which is a typical hazard-prone, mountainous area. We identified fourteen influential factors based on field measurements to describe the “avalanche–landslide–debris flow” hazard chains in the study area. We constructed training (80%) and validation (20%) datasets for 378 hazard sites. The performance of the models was assessed using standard statistical metrics, including recall, confusion matrix, accuracy, F1, precision, and area under the operating characteristic curve (AUC), based on a multicollinearity analysis and Relief-F two-step evaluation. The results indicate that all three models, i.e., RF, GA-RF, and Bayes-RF, achieved good performance (AUC: 0.89~0.92). The Bayes-RF model outperformed the other two models (AUC = 0.92). Therefore, this model is highly accurate and robust for mountain hazard susceptibility assessment and is useful for the study area as well as other regions. Additionally, stakeholders can use the susceptibility map produced to guide mountain hazard prevention and control measures in the region. Full article

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides. Full article

As a common geological hazard, debris flow is widely distributed around the world. Meanwhile, due to the influence of many factors such as geology, geomorphology and climate, the occurrence frequency and main inducing factors are different in different places. Therefore, the evaluation of debris flow sensitivity can provide a very important theoretical basis for disaster prevention and control. In this research, 43 debris flow gullies in Changping District, Beijing were cataloged and studied through field surveys and the 3S technology (GIS (Geography Information Systems), GPS (Global Positioning Systems), RS (Remote Sensing)). Eleven factors, including elevation, slope, plane curvature, profile curvature, roundness, geomorphic information entropy, TWI, SPI, TCI, NDVI and rainfall, were selected to establish a comprehensive evaluation index system. The watershed unit is directly related to the development and activities of debris flow, which can fully reflect the geomorphic and geological environment of debris flow. Therefore, the watershed unit was selected as the basic mapping unit to establish four evaluation models, namely ACA–PCA–FR (Analytic Hierarchy Process–Principal Component Analysis–Frequency Ratio), FR (Frequency Ratio), SVM (Support Vector Machines) and LR (Logistic Regression). In other words, this research evaluates debris flow susceptibility by comparingit with two traditional weight methods (ACA–PCA–FR and FR) and two machine learning methods (SVM and LR). The results show that the SVM evaluation model is superior to the other three models, and thevalueofthe area under the receiver-operating characteristic curve (AUC) is 0.889 from the receiver operating characteristic curve (ROC). It verifies that the SVM model has strong adaptability to small sample data. The study was divided into five regions, which were very low, low, moderate, high and very high, accounting for 22.31%, 25.04%, 17.66%, 18.85% and 16.14% of the total study area, respectively, by SVM model. The results obtained in this researchagree with the actual survey results, and can provide theoretical help for disaster prevention and reduction projects. Full article