Search Results (550)

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article

Heavy rainfall events in the southern Anhui region are the main meteorological disasters, often leading to floods and secondary disasters. This article explores the mechanisms supporting extreme precipitation by studying the spatiotemporal characteristics of heavy rainfall events during 2022–2024 and their related atmospheric circulation patterns. Using high-resolution precipitation data, ERA5 and GDAS reanalysis datasets, and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model analysis, the main sources and transport pathways of water that cause heavy rainfall in the region were determined. The results indicate that large-scale circulation systems, including the East Asian monsoon (EAM), the Western Pacific subtropical high (WPSH), the South Asian high (SAH), and the Tibetan Plateau monsoon (PM), play a decisive role in regulating water vapor flux and convergence in southern Anhui. Southeast Asia, the South China Sea, the western Pacific, and inland China are the main sources of water vapor, with multi-level and multi-channel transport. The uplift effect of mountainous terrain further enhances local precipitation. The Indian Ocean basin mode (IOBM) and zonal index are also closely related to the spatiotemporal changes in rainfall and disaster occurrence. The rainstorm disaster risk assessment based on principal component analysis, the information entropy weight method, and multiple regression shows that the power index model fitted by multiple linear regression is the best for the assessment of disaster-causing rainstorm events. The research results provide a scientific basis for enhancing early warning and disaster prevention capabilities in the context of climate change. Full article

Extreme rainfall events characterized by small catchments with high-velocity flows pose critical challenges to infrastructure resilience, particularly the rail infrastructure, due to its partial location near rivers and in mountainous regions, and the limited availability of alternative routes. This can lead to severe damages, often resulting in long-term route closures. To mitigate flash flood damage, detailed information about affected structures and damage processes is necessary. Therefore, this study presents a newly developed multi-criteria flash flood damage assessment framework for the rail infrastructure and a QGIS-based analysis of the most frequent damages. Applying the framework to Eifel route damages in Western Germany after the July 2021 flood disaster shows that nearly 45% of the damages affected the track superstructure, especially tracks and bedding. Additionally, power supply systems, sealing and drainage systems, as well as railway overpasses or bridges, were impacted. Approximately 30% of the railway section showed washout of ballast, gravel and soil. In addition, deposit of wood or stones occurred. Most damages were classified as minor (47%) or moderate (34%). Furthermore, damaged track sections were predominantly located within a 50 m distance to the Urft river, whereas undamaged track sections are often located at a greater distance to the Urft river. These findings indicate that the proposed framework is highly applicable to assess and classify damages. Critical elements and relations could be identified and can help to adapt standards and regulations, as well as to develop preventive measures in the next step. Full article

Landslide runout distance is governed not only by collapsed magnitude but also by site-specific geoenvironmental conditions. While remote sensing techniques has advanced landslide susceptibility mapping, its application to runout modeling remains limited. This study examined the role of collecting data on various site conditions through remote sensing and field surveys datasets in predicting the landslide travel distance from the 2022 disaster in Petrópolis, Rio de Janeiro. A total of 218 multivariate linear regression models were developed using morphometric, remote sensing, and field survey variables collected across collapse, transport, and deposition zones. Results show that predictive accuracy was limited when based solely on landslide scale (R² = 0.06–0.10) but improved substantially with the inclusion of site condition data across collapse, transport, and deposition zones (R² = 0.49–0.51). Additionally, model performance was strongly influenced by runout path typology, with channelized flows producing the most stable and accurate predictions (R² = 0.73–0.90), while obstructed and open-slope paths performed worse (R² = 0.39–0.61). These findings demonstrate that empirical models integrating multizonal site-condition data and runout path typology offer a scalable, reproducible framework for landslide hazard mapping in data-scarce, complex mountainous urban environments. Full article

Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article

The escalating consequences of human activities and global warming have markedly increased the frequency and intensity of geological disasters worldwide, posing a formidable threat to human life and property. In the southern mountainous region of Ningxia, China—an area characterized by complex topography, interlaced ravines, and pronounced ecological fragility—recurrent geological disasters have substantially constrained rural revitalization and development. This study introduces the integration of the Information Value (IV) method with Random Forest (RF) and XGBoost models, identifying IV + XGBoost as the optimal model through rigorous ROC-curve validation. The results reveal that low- and lower-risk areas account for 58.63% of the total area (7644.20 km² and 4038.08 km²), medium-risk areas cover 29.24% (5825.76 km²), and high-risk regions constitute 12.13% (2417.28 km²). The latter are predominantly in river valleys with high population density and intensive economic activities. These findings provide practical recommendations for scientifically informed disaster management and decision-making by relevant authorities. Furthermore, the proposed methodology offers valuable insights for disaster risk assessment in other regions with similar complex terrains and ecological vulnerabilities, contributing to developing more effective and sustainable disaster mitigation strategies. Full article

Since the 1990s, glaciers in the Ányêmaqên Mountains of the Qinghai–Tibet Plateau have exhibited anomalous retreat and thinning. This persistent deglaciation has triggered secondary disasters including glacial debris flows, ice collapses, and glacial lake outburst floods, posing significant threats to regional ecological security and sustainable socioeconomic development. To address this issue, we conducted a comprehensive analysis of glacial morphological characteristics using multi-source time-series high-resolution remote sensing imagery spanning 2013–2024. Glacier boundaries were extracted through integrated methodologies combining manual visual interpretation, band ratio thresholding, three-dimensional geomorphic analysis, and an optimized DeepLabV3+ convolutional neural network with adaptive activation thresholds. Extraction accuracy was rigorously validated using quantitative metrics (Accuracy, Precision, Recall, Loss, and F1-score). Key findings reveal the following: dominant glacier types include ice caps, valley glaciers, and hanging glaciers distributed at mean elevations of 5200–5600 m; total glacial area decreased from 102.71 km² to 81.10 km², yielding an average annual decrease rate of −1.93%; glacier count increased from 74 to 86, corresponding to a mean relative change rate of 1.18% per annum; and thirty-eight geohazard sites were identified predominantly on upper slopes (30–50°) of north-facing terrain, with elevations ranging from 4500–5400 m (base) to 5120–6050 m (crest). These results provide critical data support for enhancing ecological resilience, strengthening disaster mitigation capabilities, and safeguarding public safety and infrastructure against climate change impacts in the region. Full article

Landslides pose a significant threat to Taiwan’s mountainous regions, particularly after extreme weather events such as typhoons. This study introduces a machine learning framework for post-disaster land use-land cover (LULC) classification and landslide detection in Nanfeng Village, central Taiwan, following Typhoon Khanun in August 2023. Using high-resolution Pléiades imagery and 22 environmental and spectral factors, a Random Forest classifier was developed. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was systematically evaluated across multiple variants. The Distance_SMOTE method yielded the best results, increasing overall accuracy from 74% to 85% and the Kappa coefficient from 0.69 to 0.82. F1-scores for landslides, roads, and grassland improved markedly, reaching 0.97, 0.85, and 0.78, respectively. The optimized model produced accurate pre- and post-typhoon LULC maps, revealing significant expansion of landslide zones after the event. This study demonstrates the practical value of combining SMOTE-based resampling with Random Forest for rapid, reliable post-disaster assessment, offering actionable insights for disaster response and land management in data-imbalanced conditions. By enabling timely mapping of hazard-affected areas and informing targeted recovery actions, the approach supports disaster risk reduction, sustainable land use planning, and ecosystem restoration. These outcomes contribute to the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by strengthening community resilience, promoting climate adaptation, and protecting terrestrial ecosystems in hazard-prone regions. Full article

The Qinba Mountain Region in southern Shaanxi, China, is both a key ecological barrier and a repository of cultural heritage, yet its traditional villages remain highly vulnerable to natural disasters. Disaster-relocation policies have reduced direct exposure to hazards but also created challenges such as settlement hollowing and weakening of cultural continuity. However, systematic studies on the resilience mechanisms of these villages and a corresponding governance framework remain limited. This study applies social–ecological resilience theory to evaluate the resilience of 57 nationally recognized traditional villages. Using a combination of Morphological Spatial Pattern Analysis (MSPA), the entropy weight method, and the geographical detector model, we construct a three-dimensional evaluation framework encompassing terrain adaptability, hazard exposure, and ecological sensitivity. The results show that the Terrain Adaptability Index (TAI) is the dominant driver of resilience, with an explanatory power of q = 0.61, while the interaction of Hazard Exposure Index (HEI, q = 0.58) and Ecological Sensitivity Index (ESI, q = 0.49) produces a nonlinear enhancement effect, significantly increasing vulnerability. Approximately 83% of villages adopt a “peripheral attachment–core avoidance” strategy, and 57% of high-resilience villages (CRI ≥ 0.85) rely on traditional clan-based networks and drainage systems to offset ecological fragility. Based on these differentiated resilience characteristics, the study proposes a three-tiered governance framework of core protection areas–ecological restoration zones–cultural corridors. While this framework demonstrates broad applicability, its findings are context-specific to the Qinba Mountains. Future studies should apply the model to other mountainous regions and integrate dynamic simulation methods to assess climate change impacts, thereby expanding the generalizability of resilience governance strategies. Full article

Excessive tailwind, threatening the safety of aircraft takeoff and landing, is one of the prominent research topics in the field of aviation meteorology. This paper analyzes the causes of tailwinds at Beijing Daxing International Airport (BDIA), based on coherent Doppler wind lidar (CDWL) and ERA5 reanalysis data. CDWL with high spatiotemporal resolution is utilized to detect variations in the low-level wind field in the vicinity of airport areas. ERA5 reanalysis data are employed to investigate the distribution characteristics of meteorological elements such as wind fields, pressure, and temperature in the Beijing surrounding regions. The study of two typical tailwind events reveals that frontal activity, through the combined effects of pressure gradient adjustment and topographic constraints from the Taihang Mountains, drives the development of low-level southerly jets. It serves as the key mechanism triggering excessive tailwind. By integrating CDWL and ERA5 data for local and regional analysis, this study contributes to enhancing understanding of tailwind causal mechanisms and provides critical support for aviation meteorological disaster early warning. Full article

To explore the spatiotemporal evolution of extreme temperature events in Guangxi (1940–2023), reveal regional response mechanisms, and assess future trends of persistence under climate warming, a multi-scale analysis was conducted using ERA5 reanalysis data. Methodologies included RH tests for homogeneity correction, collaborative kriging for data optimisation, Mann–Kendall tests for trend and abrupt change detection, Morlet wavelet analysis for cyclic pattern identification, Exploratory Spatio-Temporal Data Analysis (ESTDA) for spatial heterogeneity quantification, and Rescaled Range (R/S) analysis to calculate Hurst indices for future persistence assessment. Results showed the following: (1) The ERA5 dataset exhibited high applicability in Guangxi (R = 0.9989, RMSE = 1.9492 °C), supporting robust evidence of continuous warming—warm indices (e.g., SU25, TX90p) increased significantly (SU25 at 0.2044 d/10a), while cold indices (e.g., TN10p, FD0) declined (TN10p at −0.0519 d/10a); abrupt changes of cold indices were concentrated in 1942–1950, with warm indices accelerating post-2000 and TXx exhibited the highest warming rate (0.23 °C/decade). (2) Extreme temperature indices displayed a primary 19–21-year oscillation cycle (dominant in warm indices) and a secondary 13-year cycle (prominent in cold indices). (3) Spatial heterogeneity featured northwest–southeast cold–heat inversion, coastal–inland intensity gradients, and latitudinal zonation of extreme indices; ESTDA revealed intensified polarisation, with warm indices clustering in low-latitude regions (e.g., Baise) and cold indices declining homogeneously in mountainous areas (e.g., Guilin), indicating an irreversible transition to a warming steady state. (4) R/S analysis indicated all indices had Hurst indices of 0.65–0.92, reflecting persistent future trends consistent with historical evolution, with warm indices (e.g., TNn, SU25) showing stronger persistence (H > 0.85). This work clarifies the spatial polarisation mechanism and future persistence of extreme temperature dynamics in Guangxi, providing a multi-scale scientific basis for disaster early warning and adaptation planning in climate-sensitive karst-monsoon regions. Full article

Traditional landslide monitoring systems struggle to capture the spatiotemporal dynamics of rainfall-induced hydro-mechanical processes, with a significant risk of signal loss during critical “unsaturated-saturated” state transitions. To address this issue, we propose an integrated framework that utilizes FLAC3D numerical simulation to dynamically optimize multi-sensor deployments. Through coupled seepage-stress analysis under different rainfall scenarios in China’s Tianshan Mountains, this study achieved the following objectives: (1) risk-based sensor deployment by precisely identifying shallow shear strain concentration zones (5–15 m) through FLAC3D simulation (with FBG density of 0.5 m/point in the core sliding belt and GNSS spacing ≤ 50 m); (2) establishment of a multi-parameter cooperative early warning system (displacement > 50 mm/h, pore water pressure > 0.4 MPa, strain > 6400 με), where red alerts are triggered when at least two parameters exceed thresholds, reducing false alarm rates; and (3) development of an adaptive sampling framework based on three rainfall intensity scenarios, which increases measurement frequency during heavy rainfall to capture transient critical points (GNSS sampling rate enhanced to 10 Hz). This approach significantly enhances the capture capability of critical hydro-mechanical transition processes while reducing the monitoring redundancy. The framework provides a scientifically robust and reliable solution for slope disaster-risk prevention and management. Full article

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions. Full article

The increasing frequency of extreme weather events and rapid urbanization has exacerbated pluvial flood risks, underscoring the urgent need to strengthen the assessment of pluvial flood resilience in China’s southwestern mountainous regions. Kunming—a plateau basin city—was selected as a case study, and an urban pluvial flood resilience assessment system was developed based on the DPSIR model. The analytic hierarchy process (AHP), entropy method, and game theory-informed combination weighting were applied to determine indicator weights, while the extension cloud model was utilized to quantitatively assess resilience evolution from 2013 to 2022. The results reveal that: (1) Kunming’s pluvial flood resilience experienced a clear three-stage evolution—initial construction (Level II), resilience enhancement (Level III), and resilience reinforcement (Level IV)—reflecting a transition from rudimentary resilience to advanced adaptive capacity; (2) the ranking of primary indicator weights is as follows: Driving Forces > Pressure > State > Response > Impact, with Flood Disaster Risk (P6), Flood Disaster Early Warning Capability (R1), and Topographic and Geomorphological Characteristics (P7) identified as key influencing factors; (3) marked disparities exist across the five dimensions: the Driving Forces dimension demonstrates increasing economic support; the Pressure dimension reflects structural vulnerabilities and climate variability; the State and Impact dimensions advance incrementally through policy implementation; and the Response dimension has substantially improved due to smart city technologies, although persistent gaps in inter-agency emergency coordination remain. This research offers a scientific basis for enhancing pluvial flood resilience in southwestern mountainous cities. Full article

Rainfall-induced slope failures are among the most frequent and destructive natural hazards in Japan’s mountainous regions, often causing severe loss of life and damage to infrastructure. This study presents an integrated statistical framework for regional-scale landslide hazard mapping, with a focus on 0-order basins. To enhance spatial prediction accuracy, both bivariate and multivariate statistical models are employed. Bivariate models efficiently assess the relationship between individual conditioning factors and landslide occurrences but assume variable independence. Conversely, multivariate models account for multicollinearity and the combined effects of interacting factors, although they often require more complex data processing and may lack spatial clarity. To leverage the strengths of both approaches, two hybrid models were developed and applied to a 242.94 km² area in Fukuoka Prefecture, Japan. Model validation was performed using a matrix-based evaluation supported by a threshold optimization algorithm. Among the models tested, the hybrid Frequency Ratio–Logistic Regression (FR + LR) model demonstrated the highest predictive performance, achieving a success rate of 84.30%, a false alarm rate of 17.88%, and a miss rate of 12.30%. It effectively identified critical slip surfaces within zones classified as ‘High’ to ‘Very High’ susceptibility. This integrated approach offers a statistically robust, scalable, and interpretable solution for landslide hazard assessment in geomorphologically complex terrains. It provides valuable support for regional disaster risk reduction and contributes directly to achieving the Sustainable Development Goals (SDGs). Full article