GeoHazards

The role of land use/land cover (LULC) dynamics in predisposing slopes to shallow landsliding is widely acknowledged but seldom translated into operational susceptibility modelling. Most data-driven approaches still treat LULC as a static factor, neglecting the legacy effects of recent transitions. This study presents a methodological framework to quantify the influence of multi-temporal LULC changes on shallow landslide initiation and to incorporate this information into susceptibility mapping. The procedure was tested in the Metropolitan City of Messina (formerly known as the Province of Messina), Southern Italy, a representative Mediterranean area repeatedly affected by rainfall-triggered slope failures. Freely available LULC maps from 1990 to 2006 were processed through post-classification change detection to identify dominant land cover trajectories. Preliminary analyses within buffer areas showed higher landslide indices (LI, LAI) and Frequency Ratios for some transition classes, suggesting a potential role of LULC changes. These findings motivated the comparison between a static LULC configuration and a dynamic one incorporating the detected transitions within a Frequency Ratio susceptibility model. The dynamic model did not improve the mean Area Under the Curve (AUC) compared to the static model (0.7774 vs. 0.7742), and the observed reduction in variability across five independent random splits (standard deviation 0.012 vs. 0.064) should be considered preliminary. The proposed workflow, based entirely on open data and GIS-based processing, offers a transparent and reproducible methodology for integrating LULC transitions into dynamic susceptibility maps. The use of higher-resolution input data could potentially reduce the scale mismatch and improve the detection of fine-scale transitions, supporting more effective landslide risk mitigation and evidence-based land planning. Full article

The shear behavior of loess is closely linked to its microstructural evolution, and understanding this relationship is essential for deciphering the mechanisms of loess hazards. In this study, consolidated-drained (CD) triaxial tests were conducted on intact Q₃ Malan loess from the Dongzhi tableland, China, under varying water contents and confining pressures. Scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) analyses were performed on specimens before and after shearing to quantitatively and qualitatively characterize the changes in pore and particle properties and their connection to shear deformation. The results reveal three failure modes, including shear, homogeneous, and plastic failure. They are governed by the combined effects of microstructural variation and microcrack development, depending on confining pressure and water content. Quantitatively, as water content increases from 9% to 20%, cohesion decreases by 86.8% and peak shear strength reduces by 68.4%, while the internal friction angle decreases only slightly. Water-induced strength deterioration is governed primarily by cohesion loss rather than friction angle reduction. Thus, 20% water content was identified as the critical threshold marking the transition from cohesion-dominated to friction-dominated strength degradation. A critical threshold at approximately 27% water content is identified, beyond which about 70% of mesopore and macropore volumes undergo collapse, after which the strength is almost entirely sustained by interparticle friction. Based on these findings, the water-induced strength decay mechanism is categorized into three stages: rapid cement degradation, friction-dominated transition, and slow attenuation. These macroscopic phenomena are closely linked to the continuous adjustment of the microstructure, manifested by the softening, dispersion, and disintegration of cementations, particle movement and rearrangement, and the reduction and mutual transformation of inter-aggregate pores under loading and wetting. The three-stage mechanism and threshold characteristics of loess strength degradation upon wetting revealed in this study can provide theoretical support for early slope-instability warning in loess irrigation and heavy rainfall regions, as well as engineering reinforcement prioritizing the recovery of cohesion. Full article

This study presents an integrated statistical framework for identifying representative large-scale climate teleconnection indices associated with total burned area and for supporting the selection of climate predictors in wildfire-related statistical models. A large set of seasonally resolved climate indices, including the North Atlantic Oscillation (NAO—two versions), the Arctic Oscillation (AO), the Atlantic Multidecadal Oscillation (AMO), the Mediterranean Oscillation (MO—two versions), the East Atlantic–West Russia pattern (EAWR), the Tropical North Atlantic (TNA), and the Atlantic Meridional Mode (AMM), was examined. Because many of these indices describe related atmospheric and oceanic processes, dimensionality reduction and predictor selection were required to limit multicollinearity. Principal component analysis (PCA) was first used to identify groups of interrelated climate indices, followed by partial correlation analysis to distinguish redundant predictors from those retaining independent information with respect to total burned area. Finally, LASSO regression was applied to evaluate the relative explanatory contribution of candidate indices and to perform automatic variable selection. The PCA solution identified ten rotated components explaining 82.31% of the total variance. The results indicate that several seasonal NAO and MO indices contain highly overlapping information, whereas selected indices, particularly MOI2 spring and MOI2 summer, retain comparatively stronger independent associations with total burned area. The integrated PCA–partial correlation–LASSO framework provides a systematic approach for reducing redundant climate predictors and identifying large-scale climate signals that may be informative for understanding variability in total burned area and for supporting statistical analyses of wildfire–climate relationships. Full article

Unstable rock blocks and cliffs pose a widespread geohazard, and their mechanical state can be tracked through their ambient-vibration resonance frequencies, whose decrease anticipates progressive failure. Such monitoring is usually performed with expensive broadband instrumentation, limiting spatial and temporal coverage. Here we assess whether a low-cost, IoT-enabled node—built around a Raspberry Pi single-board computer, a 24-bit sigma-delta digitiser and a force- balance accelerometer (Geobit FBA-200)—can identify the resonance of an unstable coastal rock block at the celebrated “moving rock” of Kounopetra (Paliki peninsula, Kefalonia, Greece), a site historically renowned for visually perceptible rocking boulders. We stress that the low-amplitude 7.7 Hz structural eigenvibration characterised here is a distinct phenomenon from the historically documented ∼0.3 Hz macroscopic, quasi-rigid rocking of the boulder: the former is the ambient–vibration resonance of the fractured rock mass, the latter a large-amplitude rigid-body oscillation. Two identical nodes recorded ground acceleration simultaneously for nine hours: one on the fractured Kounopetra rock mass and one on stable ground 25 m away, used as a reference. The rock station exhibits a clear, temporally stable fundamental resonance at f₀ = 7.7 Hz (Q ≈ 50, damping ζ ≈ 1%), amplified by up to an order of magnitude relative to the reference and entirely absent from it, whereas a narrow 20.5 Hz line present on both nodes is identified as instrument-related and discarded. A simultaneous two-station analysis further shows that the ambient sources are extremely local (only 0.4% of transient activity is common to the two nodes 25 m apart), quantifying a design constraint for differential schemes. The results demonstrate that a low-cost force-balance node is sufficient to establish a resonance baseline for an unstable rock block, opening the way to dense, affordable early-warning networks; the main limitations are the single vertical component and the short record, which preclude polarisation analysis and long-term tracking of f₀. Full article

On 18 May 2026, a M5.2 double earthquake struck the Taiyangzhen area of Liunan District, Liuzhou City, Guangxi, China, triggering shallow surface collapses in this karst terrain. We conducted three UAV orthophoto surveys of the meizoseismal area on 20 May, 23 May, and 24 May, and interpreted 17 collapse monitoring units from the imagery. The total collapse area increased from 75.3 m² on 20 May to 499.1 m² on 23 May, and further to 553.9 m² on 24 May. On 20 May, only 7 of 17 units exhibited collapses; by 23 May, all 17 units were affected. Among them, 7 pre-existing collapse patches expanded, and 10 new collapses emerged between 20 and 23 May. Depth measurements revealed measurable depths of 0.18–7.60 m for 7 collapses, and all 6 units with bi-temporal depth data showed continued deepening from 23 to 24 May. Ponding water was observed in up to 10 units, consistent with the 98.7 mm of rainfall recorded during 18–24 May. Multi-temporal UAV surveys reveal that post-seismic surface collapse development in karst terrain extends well beyond the mainshock, with both rapid expansion of pre-existing failures and delayed emergence of new collapses driven by the coupled effects of seismic weakening and hydrologic forcing. Full article

Weathered marl hillslopes fail repeatedly across peri-urban Morocco, yet engineers investigating them rarely know which soil or geometric property most deserves their limited testing budget. This study answers that question for the landslide-prone slopes of Moulay Yacoub, in the northern pre-Rifian domain, by ranking the sensitivity of the factor of safety (FoS) to cohesion (c), friction angle (φ), unit weight (γ), slope height (H) and slope angle (β) under dry, deep-water-table conditions. Latin hypercube sampling generated 400 configurations over ranges drawn from site data; the FoS of each was computed by Bishop’s simplified method in Talren, and regional sensitivity analysis—the two-sample Kolmogorov–Smirnov statistic with a relative sensitivity index—ranked the five inputs. Cohesion governs the response by a wide margin (D = 0.470; 38.2% of total sensitivity at FoS = 1.2), ahead of slope height and friction angle (D ≈ 0.26); slope angle is marginal, and unit weight is not discriminating. Repeating the analysis at four thresholds (1.1–1.4) shows that the primacy of cohesion is threshold-independent, and that the apparent significance of slope angle at stricter thresholds is a statistical power effect, not a mechanical one. Site investigation in comparable marl settings should prioritise cohesion characterisation. Full article

Armenia is located within the Arabia–Eurasia collision zone and is exposed to a significant seismic hazard associated with active fault systems capable of generating destructive earthquakes. The 1988 Spitak earthquake highlighted the vulnerability of Armenian urban areas and the need for reliable seismic risk assessment methods. This study presents the first harmonized scenario-based seismic risk assessment framework for six major Armenian cities by integrating seismotectonic source characterization, deterministic ground-motion modeling, locally derived Vs30-based site characterization, GIS-based exposure modeling, and vulnerability assessment within the ELER (Earthquake Loss Estimation Routine) platform. Vulnerability functions were adapted to Armenian building typologies and calibrated using observed damage from the 1988 Spitak earthquake. Deterministic earthquake scenarios (Mw 6.5–7.3) were developed based on the seismic potential of the country’s principal active fault systems. The results reveal substantial spatial variability in seismic risk controlled by differences in ground-motion intensity, local site conditions, building vulnerability, and population exposure. Masonry-dominated urban areas exhibit the highest relative structural losses, whereas Yerevan experiences the greatest absolute losses because of its large population and concentrated building stock. Severe damage and collapse (D4–D5) may affect more than 20–25% of buildings in the most vulnerable cities. Validation against observed 1988 earthquake damage demonstrates the applicability of the proposed framework for seismic risk reduction, emergency preparedness, and long-term urban resilience planning in Armenia. Full article

The southeastern Tibetan engineering corridor hosts the densest transportation network in Tibet, China, and is traversed by large-scale railway and power corridor projects under construction. This region is home to numerous glacial lakes, some of which are prone to glacial lake outburst floods (GLOFs), posing potential threats to the infrastructure. However, the spatiotemporal evolution and GLOF susceptibility of these lakes remain unclear. Using Landsat 5–9 and Sentinel-2 satellite imagery, we analyzed the spatiotemporal characteristics of glacial lakes from 1990 to 2020. Based on historical GLOF events, we established a susceptibility assessment criterion and determined the susceptibility levels of all glacial lakes in the study area. Results show that the number and area of glacial lakes increased by 40.4% and 26.2%, respectively, from 1990 to 2020, with expansion rates of 2.47 lakes/year and 0.26 km²/year. We identified 31 very highly and 48 highly susceptible lakes, mainly distributed along the Gongrigabu River and the Parlung Tsangpo River. Among them, 35 lakes are most likely to impact National Highways G219 and G318 within the study area. Additionally, three channels with glacial lake clustering amplification effects were found, which may lead to the superposition and amplification of flood impacts, significantly increasing GLOF risks and hazards. Our findings provide crucial references for ensuring the safe operation of local transportation networks and reducing GLOF risks in ongoing large-scale construction projects. Full article

Arduino-based creepmeters utilizing a Linear Variable Differential Transformer (LVDT) and ultrasonic sensors were fabricated to monitor displacement changes along the creeping segment of the West Valley Fault (WVF) in southeastern Metro Manila, Philippines. Along with a custom-assembled, Arduino-based rain gauge, these instruments were initially intended to prevent data gaps during the COVID-19 pandemic when commercial data recorders experienced operational downtime. However, they have since proven to be cost-effective alternatives for determining short-term slip rates and monitoring displacement variations driven by episodic and seasonal precipitation changes. The LVDT creepmeter provides higher accuracy for displacement and slip rate determination. Conversely, the ultrasonic creepmeter is better suited for tracking abrupt displacement changes and, to some extent, longer-term displacement trends as it is more sensitive to environmental conditions. Deploying low-cost monitoring instruments in active fault regions bridges critical data gaps and improves the understanding of creep triggers and mechanisms. Although vertical creep occurs along pre-existing tectonic features of the WVF creeping segment, our creepmeter monitoring reveals sustained, accelerated creep within its southern portion. This localized movement is driven primarily by nontectonic forces—chiefly groundwater extraction, with episodic and seasonal precipitation influences. Consequently, this implies a continued ground rupture hazard and the potential for induced seismicity. Full article

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks in vulnerable regions like the Sebeya Catchment. This study adopted a GIS-based Analytical Hierarchy Process (GIS-AHP) integrated with local community knowledge to evaluate flood susceptibility using 10 conditioning factors: Topographic Wetness Index (TWI), Elevation, Rainfall, Slope, Land use/Land cover (LULC), Soil types, Normalized Difference Vegetative Index (NDVI), Distance to roads, Distance to rivers, and drainage density. These factors were selected based on their established influence on flood susceptibility as identified through literature review, expert consultation, and local community experience in the flood-affected zones. Spatial datasets were gathered from remote sensing platforms, Digital Elevation Models, Meteorological records, and existing geospatial databases, and were processed within a GIS environment. The pairwise comparison matrix of the AHP was used to derive weighting coefficients representing the relative contribution of each factor in inducing flood, with Rainfall (0.23), Slope (0.15), Distance to river (0.12), drainage density (0.12), and Elevation (0.11) as the most influential criteria. The findings revealed that 88.4% of the study area falls within a moderate flood-susceptible zone, whereas 6.4% and 5.2% fall within high and low susceptible zones, respectively. The current study indicates that damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate 0.97 as an overall accuracy, confirming strong model performance and reliability. The proposed adaptive strategies for enhancing flood resilience include improvement in land use planning, use of early warning systems, and sustainable catchment management. Full article

Data-driven models are increasingly used for flood and landslide susceptibility mapping in rapidly urbanizing regions, particularly in the Global South. In this context, land use and land cover (LULC) is routinely adopted as a conditioning factor, although its geomorphological meaning and temporal consistency with hazard inventories are seldom evaluated. Information Value (IV) models have been widely applied to landslide susceptibility, but their use for flood susceptibility in complex coastal cities remains limited. This study evaluates IV-based flood and landslide susceptibility in Vitória, Brazil, a predominantly insular city characterized by sharp geomorphological contrasts and high population density. Two LULC datasets with different levels of urban detail were tested as conditioning factors alongside topographic and hydrological variables. Eighteen models were constructed for each hazard and validated using Area Under the Curve (AUC) metrics and expert judgement. Morphological attributes were the most important predictors: slope alone achieved an AUC of 0.90 for landslides, whereas elevation reached 0.71 for floods. The inclusion of LULC increased AUC values to 0.94–0.95 for landslides and 0.85–0.89 for floods, but also introduced spatial and temporal biases associated with stationary and coarsely classified land cover. Our findings highlight the limitations of incorporating LULC as a conditioning factor without temporal harmonization with hazard inventories or adequate urban class disaggregation. We argue that, in complex urban settings, LULC is more appropriately interpreted as a proxy for exposure and vulnerability than as a dominant predisposing factor, and that its use in predictive models should be critically assessed to avoid misleading conclusions. Full article

Mountainous road corridor landslides pose major dangers to infrastructure and transportation networks in areas with rugged terrain, fractured limestone lithology, and road-induced instability. This research presents a segment-based Random Forest (RF) model to evaluate landslide susceptibility along a mountainous road corridor spanning about 20 km in northeastern Iraq. Ten conditioning factors for landslide susceptibility were determined using Google Earth Engine (GEE) and GIS analysis. The model, validated using a 70/30 train/test split, achieved a mean cross-validation AUC of 0.783 ± 0.072 and an independent test AUC of 0.725 (accuracy = 0.735; Cohen’s Kappa = 0.401; recall = 0.750). To carry out independent multi-scale validation, the RF susceptibility maps were compared with a high-resolution SLAM LiDAR–AHP susceptibility approach within an overlapping ~2 km subsection, providing cross-scale validation of corridor-scale RF susceptibility predictions using a high-resolution susceptibility mapping framework. Comparison of the two approaches showed high spatial agreement, with 88.9% of the 18 overlapping segments exhibiting exact or one-class agreement between the two approaches. Full article

The slopes of the Champagne vineyards are regularly affected by landslides. Given the high societal and economic stakes, these processes cause significant damage and pose a major challenge for the wine industry, forestry and heritage preservation. Numerous studies have already been conducted to understand their behavior and hydrodynamic functioning. They show varied morphologies and a dominant influence of water resources. However, this forcing does not explain the spatial distribution of current landslides, which occur in upper-slope positions, on the steepest terrain recently planted with vines. Using the two shallow landslides at Reuil, located in the heart of the Marne Valley in the Champagne vineyards, as a study site, recent landslide activity is analyzed through a comparative analysis of three DTMs derived from LiDAR HD data and two UAV photogrammetric surveys. This study reveals the affected areas and displaced volumes, which can reach up to 900 m³. Landslide activity was then correlated with regional climatic data. This research shows that landslides are more likely to occur during a period of excessive rainfall following a drier period. Diachronic analysis of aerial images also demonstrates the influence of land use on landslide activity. In particular, land clearing and vineyard operations on the steepest plots constitute a significant anthropogenic forcing affecting slope stability. Taken together, these results provide greater insight into the changing geomorphological dynamics of the Champagne vineyards. On the one hand, they clarify how landslides occur at vineyard plot scale. On the other hand, they provide initial insights into the resilience of stakeholders (winegrowers, etc.) affected by these instabilities. Full article

Based on urban subway foundation pit projects in typical limestone areas, this paper employs 3DEC numerical simulation to investigate the effects of the thickness and burial depth of weak interlayers on the deformation characteristics and stability of the dip slope and anti-dip sides of rock foundation pits. The results reveal asymmetric deformation responses between the two slope types. The anti-dip side undergoes bending and toppling deformation; the displacement within the weak interlayer is greater than that of the overlying rock mass, and the spatial position of the weak interlayer governs the maximum deformation of the anti-dip side. In comparison, the thickness and burial depth of the weak interlayer exert a significant influence on the horizontal displacement of the dip slope side. Two quantitative critical thresholds are identified: (1) an interlayer thickness threshold—when t ≥ 4 m, the extrusion displacement of the weak interlayer is distinctly larger than that of the adjacent hard rock, while t ≤ 3 m shows no obvious displacement difference due to the clamping effect of surrounding rock—and (2) a burial depth threshold—slope displacement rises sharply when interlayer burial depth reaches 22.66~26.15 m, while shallow-buried interlayers (12.20~19.18 m) only cause limited deformation. Additionally, the existence of weak interlayers leads to substantially larger displacement in the upper hard rock stratum relative to the lower stratum. These findings provide a reliable reference for the supporting optimization and stability design of rock foundation pits. Full article

NASA’s Fire Information for Resource Management System (FIRMS) provides near-real-time thermal anomaly detections from VIIRS, but not all detections correspond to wildfire incidents: industrial heat, agricultural burning, and sensor artifacts produce false alarms that contribute to alert fatigue for emergency-management analysts. We study whether contextual machine learning (ML) features improve wildfire-incident classification from FIRMS detections, and—more importantly—whether reported gains survive leakage-controlled evaluation. We construct a labeled dataset by matching 521,395 VIIRS SNPP detections across CONUS in 2024 to 3766 NIFC 2024 wildfire perimeters, yielding 131,771 (25.3%) wildfire-matched and 389,624 candidate non-wildfire detections spanning 1067 distinct wildfire incidents. We benchmark five operational baselines and six classifiers under four validation regimes (random, event-aware, 5° spatial-block, and temporal holdout) with and without raw geographic coordinates. A naive random split inflates LightGBM to F1 = 0.985, but a leakage-controlled event-aware split reduces it to F1 = 0.767, and a spatial-block holdout to F1 = 0.627. Feature attribution shows geographic coordinates account for 88.9% of model gain—the summed share of LightGBM’s total split-gain attributed to the three coordinate features within the full-feature model; removing coordinates improves spatial-block generalization from F1 = 0.627 to 0.818, demonstrating that raw coordinates drive memorization of where 2024 fires occurred rather than transferable discrimination. We further show that spatiotemporal clustering must be causal: a model using full-partition clustering appears strong (F1 = 0.908) but leaks future detections, whereas a properly causal trailing-window version ties plain LightGBM in-distribution (F1 = 0.762). Combining causal clustering with no raw coordinates is the most robust configuration under spatial transfer (spatial-block F1 = 0.868 vs. 0.627 for the coordinate model). Bootstrap 95% confidence intervals show these gaps far exceed statistical uncertainty, and sensitivity analyses show the conclusions are robust to the spatial-block size and to the clustering-window choice. Under natural class prevalence (14%), precision falls to 0.69, and results are sensitive to the labeling buffer. All ML models nonetheless far exceed FIRMS high-confidence thresholding (F1 = 0.128). We argue that spatial leakage—not raw accuracy—is the central methodological issue for FIRMS wildfire-incident classification, and recommend coordinate-free, causal spatiotemporal-clustering features evaluated under spatial holdout. The system is intended as an analyst-prioritization decision-support layer, not autonomous incident confirmation. Full article

The loess gully region is characterized by complex terrain with crisscrossing gullies, where coal mining can readily induce surface subsidence and slope deformation. Such deformation often leads to geological hazards and ecological issues, including collapses, landslides, soil erosion, vegetation dry up, and land degradation. Therefore, understanding the deformation behavior of mining-induced slopes is essential for the restoration and management of mine geological environments. This study focuses on five slopes within working faces 50205 and 50206 of the Zhen’er Coal Mine in Fugu County. Using a combination of 3DEC numerical simulations and orthophoto-based fracture identification, we systematically investigated mining-induced slope deformation under the complex topographic conditions of the loess gully region. The goal is to answer three key questions: where mining-induced slope deformation primarily occurs, how it evolves over time, and what the main controlling factors are. Spatially, the primary deformation zones and their propagation paths vary significantly among the five slopes. The largest deformation occurs in the slope body directly above the main section of the working face, gradually decreasing toward the edges of the working face. Temporally, mining-induced slope deformation exhibits a time lag, meaning that surface responses lag behind underground mining activities and continue to develop even after the working face is fully extracted. In the loess gully region, slope deformation induced by mining is controlled not only by mining activities but also by topographic factors such as slope shape, aspect, gradient, and height. The spatiotemporal evolution of deformation becomes even more complex for slopes that span multiple working faces. These findings provide a scientific basis for monitoring mining-induced slope deformation and preventing geological disasters in the loess gully region, while also offering practical guidance for safe mining operations and hazard control in similar settings. Full article

Landslide susceptibility assessment is essential for disaster-risk reduction, land-use regulation, and territorial spatial planning in mountainous and hilly regions. However, the practical application of machine learning-based susceptibility models is often limited by the trade-off between predictive accuracy and model interpretability, as well as the instability of factor importance across different algorithms and study areas. Taking Longchuan County in northeastern Guangdong Province, China, as a case study, this research develops a comparative explainable machine learning framework to evaluate landslide susceptibility and examine the cross-model stability of SHAP-based factor attribution under local geo-environmental conditions. Fifteen conditioning factors were initially derived from multi-source geological, topographic, hydrological, environmental, and anthropogenic datasets. After multicollinearity screening using Pearson correlation analysis, twelve key factors were retained for model construction. A total of 363 historical landslide points and an equal number of non-landslide samples were divided into training and testing datasets using a stratified 70:30 sampling strategy. Eight machine learning models were optimized through grid-search parameter tuning and then comparatively evaluated. The results show that all models achieved strong predictive performance, with test-set AUC values exceeding 0.938. Among them, the Gradient Boosting Decision Tree model performed best, with an AUC of 0.9520 and the most stable control of overfitting, followed closely by CatBoost with an AUC of 0.9512. SHAP-based interpretation further revealed that the normalized difference water index, relief, and distance to rivers were the dominant factors controlling landslide susceptibility in the study area, with the normalized difference water index serving as a key explanatory factor across models. The proposed framework improves the transparency and reliability of landslide susceptibility assessment and provides a methodological reference for localized, explainable machine learning applications in geohazard risk management. Full article

Landslide susceptibility mapping (LSM) is an essential tool for hazard assessment and land-use planning in landslide-prone areas. This study compares three statistical models—Frequency Ratio (FR), Weight of Evidence (WoE), and Logistic Regression (LR)—with six machine-learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), k-Nearest Neighbor (KNN), and Decision Tree (DT), for regional landslide susceptibility assessment in Hiroshima Prefecture, Japan. A balanced dataset comprising 1936 landslide and 1936 non-landslide samples was developed from the 2018 rainfall-induced landslide inventory, utilizing seven conditioning factors: slope angle, profile curvature, aspect, elevation, lithology, soil water index, and 24 h cumulative rainfall. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score. Among the statistical models, WoE exhibited the highest performance, while SVM provided the most balanced results among the machine-learning models. Both modeling approaches consistently identified lithology and slope angle as the primary controls on landslide occurrence. Independent validation demonstrated comparable predictive performance for both models; however, spatial validation showed that WoE assigned 96.72% of observed landslides to the High and Very High susceptibility classes, compared to 72.54% for SVM. These findings underscore the importance of integrating conventional classification metrics with spatial validation to enhance the evaluation and interpretation of landslide susceptibility models for regional hazard assessment. Full article

This study presents DEDICA (Database of Hydrogeological Instability Events in Calabria, southern Italy), developed by the District Basin Authority of the Southern Apennines (ABDAM) in collaboration with the CNR-IRPI. The database integrates digitized historical sources, chronicle-based records, and previously unpublished archival data that were systematically analyzed, validated, and georeferenced within a GIS environment. After two years of development, DEDICA includes 5329 landslides, 2097 flood events, and 1711 urban flooding occurrences spanning the period 1900–2025. The system supports continuous data updating, enabling both the integration of recent events and the refinement of historical records. The database provides a comprehensive tool for identifying areas prone to geo-hydrological hazards based on historical recurrence, supporting hazard assessment, land-use planning, and risk management strategies. The methodological framework, database structure, and data processing workflow are described in detail. Spatio-temporal analyses highlight the distribution of instability processes, identifying the most affected sectors and revealing seasonal patterns and long-term trends. DEDICA represents a pilot initiative within a broader program aimed at extending the inventory to all regions under ABDAM jurisdiction, ultimately contributing to the development of a unified geo-hydrological hazard database for southern Italy. Full article