Search Results (277)

As a form of living cultural heritage, local residential landscapes manifest the essence of long-term, resilient human–land interactions. The Wuling Corridor, a vital ethnic and cultural passage connecting the Central Plains with Southwest China in Chinese history, serves as a crucial region for the mixed residence and cultural exchange of Tujia, Miao, Dong, Han, and other ethnic groups. Within this region, Ganlan stands as both the most representative vernacular architectural heritage and a residential form that is still extensively used, constituting a continuous and unique residential landscape. The spatial distribution patterns of Ganlan are the physical witness of the history of ethnic groups adapting to the complex topographic and cultural conditions. Current research focuses on the case description of single Ganlan forms, failing to systematically investigate the spatial formation mechanisms of Ganlan as a residential landscape from a geographical continuum perspective. Therefore, this study establishes a geographical database encompassing 9425 Ganlan samples from the Wuling Corridor. It integrates the geographic information system (GIS) with clustering algorithms to systematically identify the distribution patterns of Ganlan within specific geographic–cultural units and their coupling relationships with natural environments. It conducts quantitative analysis on the key driving factors concerning the emergence and evolution of Ganlan in the study area; the findings reveal the following: (1) Ganlan buildings exhibit a spatially aggregated distribution pattern along major water systems, demonstrating characteristics of multi-ethnic sharing and spatial interweaving. (2) Their distribution is constrained by natural geographical factors and influenced by the transmission pathways of construction techniques during ancient ethnic migrations to the southwest China. (3) Within multi-ethnic settlement structures, inter-ethnic cultural interactions (particularly with Central Plains culture) serve as a key driving force for the typological evolution of Ganlan. (4) The evolutionary lineage of “full-Ganlan,” “semi-Ganlan,” and “courtyard-style Ganlan” systematically demonstrates the dynamic adaptive capacity of local residential systems. Additionally, by integrating massive Ganlan heritage data with multiple spatial analysis methods, the study serves as a typical case study illuminating the adaptive strategies and resilience mechanisms of Ganlan as a local residential landscape formed in response to the environmental conditions and social changes. Also, it provides a scientific basis for the holistic conservation of architectural heritages shared by multiple ethnic groups and the integrated development of local cultural tourism industries. Full article

Forests are pivotal to hydrologic regulation, yet province-wide dynamics across complex terrain remain insufficiently quantified. We quantified Sichuan’s forest water conservation dynamics (1990–2023), coupling the InVEST water yield model with a topographic–hydraulic correction (topographic index, saturated hydraulic conductivity, land-cover-specific flow velocity). The model used precipitation and potential evapotranspiration, land-use/cover, soil texture, and rooting depth, and was calibrated to provincial water resources statistics. Outputs were stratified by elevation and slope and monetized via a replacement cost (reservoir capacity) method. Sichuan exhibited a persistent high-capacity belt along basin–mountain transitions and the southeastern ranges, contrasting with low values on the western plateau; period maxima intensified in 2020–2023. Interannual variability closely tracked precipitation anomalies against largely stable atmospheric demand; per-unit capacity declined monotonically with slope, and total capacity generally increased with elevation, with >3500 m both highest and most variable. Economic value rose overall but fluctuated and showed marked inter-city heterogeneity. We conclude that climate pacing operating on a terrain-anchored template governs Sichuan’s forest water conservation service, supporting precision, slope-aware forest management, and differentiated ecological compensation to stabilize hydrologic regulation under climate variability. Full article

This study aims to uncover the structural and functional characteristics of rhizosphere soil bacterial communities of Kengyilia thoroldiana under five types of topographic habitats in the source region of the Yellow River, and to explore the interaction mechanisms between bacterial communities and plant-soil factors, thereby providing microbiological support for the ecological restoration of Kengyilia thoroldiana artificial grasslands in alpine desert grassland. In this study, high-throughput sequencing technology was employed to compare the species composition, diversity, interaction networks, and functional characteristics of rhizosphere bacterial communities of Kengyilia thoroldiana across five topographic habitats in the source region of the Yellow River. Additionally, Mantel tests and redundancy analysis (RDA)) were conducted to explore the key environmental factors driving the structure of bacterial communities. The results showed that habitat differences significantly influenced the community characteristics of Kengyilia thoroldiana and soil physicochemical properties. The plant height, coverage, biomass, and soil carbon, nitrogen, and phosphorus contents were highest in habitats H2 and H5, while they were lowest in habitats H1 and H3. In contrast, soil pH and electrical conductivity exhibited an opposite trend. At the bacterial community level, the number of operational taxonomic units (OTUs) in habitat H5 reached 1917, with α-diversity indices such as Shannon, Ace, and Chao1 being 6.13, 1820.85, and 1844.80, respectively, significantly higher than those in habitat H1. Cluster analysis revealed that habitat H3 formed a distinct group, while the bacterial community structures in the remaining four habitats were similar. Functional prediction indicated that chemoheterotrophy and aerobic chemoheterotrophy were the dominant functions across all habitats, with functional expression values exceeding 9300 in habitats H2, H4, and H5. Redundancy analysis confirmed that soil pH and SOC were the key factors driving the structure of rhizosphere bacterial communities of Kengyilia thoroldiana. In summary, topographic habitats influence the growth of Kengyilia thoroldiana plant communities by shaping soil environmental heterogeneity, thereby regulating the structure and function of rhizosphere bacteria associated with Kengyilia thoroldiana. Full article

Machine learning algorithms are essential tools for developing Mineral Prospectivity Models (MPMs), enabling a data-driven approach to mineral exploration. This study integrated airborne geophysical, topographic, and geological data with a mineral system framework to build MPMs for iron oxide–copper–gold (IOCG) and hydrothermal nickel deposits in the Southern Copper Belt of the Carajás Province, Brazil. Seven machine learning algorithms were tested using stratified 10-fold cross-validation: Logistic Regression, k-Nearest Neighbors, AdaBoost, Support Vector Machine (SVM), Random Forest, XGBoost, and Multilayer Perceptron. SVM delivered the highest classification accuracy and robustness, highlighting new mineralized zones while minimizing false positives and negatives, and accounting for geological complexity. SHapley Additive ExPlanations (SHAP) analysis revealed that structural controls (e.g., faults, shear zones, and geochronological contacts) exert a stronger influence on mineralization patterns than lithological factors. The resulting prospectivity maps identified geologically distinct zones of IOCG and hydrothermal nickel mineralization, with high-probability closely aligned with major structural corridors oriented E–W, NE–SW, and NW–SE. Results also suggest an indirect association with volcanic units, Orosirian A1-type granites and Neoarchean A2-type granites. Full article

Beaches are important geomorphic units shaped by land–sea interactions. Changes in their profiles and surface sediments are directly influenced by both natural processes and human activities. This study is based on continuous topographic and sediment monitoring from 2021 to 2023 on the open and sheltered beaches of Puqian Bay, Hainan Island. It investigates the interannual profile evolution and the spatiotemporal response of sediment grain size under the influence of an artificial island. The results show that the Guilinyang Beach profile is mainly characterized by seasonal erosion–accretion cycles and the seaward migration of sandbars, while the Hilton Beach profile has undergone long-term erosion. At Hilton, sediment grain size changes are strongly coupled with profile erosion and accretion. Seasonal waves drive spatial differences in both profile and grain-size variation across Puqian Bay. The artificial island has reshaped local alongshore sediment transport and wave energy distribution. This has led to continuous erosion and coarsening in the open sector, while the sheltered sector remains morphologically stable. These findings reveal the spatiotemporal response patterns of headland-bay beaches under both natural and anthropogenic forcing, and provide scientific evidence for understanding coastal sediment dynamics and the impacts of artificial structures. Full article

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R²) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R² of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R² of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Wildfires in Portugal have become increasingly frequent and severe, driven by a combination of fuel accumulation, extreme meteorological conditions, and topographic complexity. This study assesses the applicability of the Hot–Dry–Windy (HDW) index in characterizing fire-weather conditions during five major wildfires: Chamusca (2003), Pedrógão Grande and Lousã (2017), Monchique (2018), and Covilhã (2022). HDW values were computed at sub-daily resolution and compared against a 1991–2020 climatology. This study also evaluates the HDW index as a high-resolution fire danger indicator in Portugal and compares it with the traditional FWI using percentile-based climatology. The findings indicate that during 12 and 15 UTC, HDW in the wildfires in Chamusca (2003) and Lousã (2017) exceeded 180–370 units, suggesting extreme air conditions driven by hot, dry, and windy weather patterns. These values denoted extremely flammable conditions since they were significantly higher than the 95th percentile. A distinct peak at 15 UTC for Pedrógão Grande (2017) topped 140 units (>P95), which is consistent with the ignition timing and a rapid beginning spread. A continuous HDW anomaly that peaked above 200 units between 2 August and 5 August preceded the Monchique (2018) event, suggesting extended heat stress and increased wind contribution. While not as severe as in previous instances, HDW at Covilhã (2022) was above the 75th percentile in the early afternoon (12–18 UTC). Results show that in all cases, HDW values exceeded the 90th and 95th percentiles during the hours of ignition and early fire spread, with the most critical anomalies occurring between 12 UTC and 18 UTC. Spatial analyses revealed regional-scale patterns of HDW exceedance, aligning with observed ignition zones. Comparisons with the Canadian Fire Weather Index (FWI) revealed that while the FWI captured seasonal fuel aridity, the HDW more effectively resolved short-term meteorological extremes, particularly wind and atmospheric dryness. The HDW index was found to identify high-risk conditions even when FWI values were moderate, highlighting its added diagnostic value. These results support the inclusion of HDW in operational fire danger rating systems for Portugal and other Mediterranean countries, where compound fire-weather extremes are becoming more frequent due to climate change. Full article

To address the negative sample selection bias and limited interpretability of traditional debris-flow event susceptibility models, this study proposes a framework that enhances generalization by integrating negative sample screening via a stacking ensemble model with an interpretable random forest. Using Wenchuan County, Sichuan Province, as the study area, 19 influencing factors were selected, encompassing topographic, geological, environmental, and anthropogenic variables. First, a stacking ensemble—comprising logistic regression (LR), decision tree (DT), gradient boosting decision tree (GBDT), and random forest (RF)—was employed as a preliminary classifier to identify very low-susceptibility areas as reliable negative samples, achieving a balanced 1:1 ratio of positive to negative instances. Subsequently, a stacking–random forest model (Stacking-RF) was trained for susceptibility zonation, and SHAP (Shapley additive explanations) was applied to quantify each factor’s contribution. The results show that: (1) the stacking ensemble achieved a test-set AUC (area under the receiver operating characteristic curve) of 0.9044, confirming its effectiveness in screening dependable negative samples; (2) the random forest model attained a test-set AUC of 0.9931, with very high-susceptibility zones—covering 15.86% of the study area—encompassing 92.3% of historical debris-flow events; (3) SHAP analysis identified the distance to a road and point-of-interest (POI) kernel density as the primary drivers of debris-flow susceptibility. The method quantified nonlinear impact thresholds, revealing significant susceptibility increases when road distance was less than 500 m or POI kernel density ranged between 50 and 200 units/km²; and (4) cross-regional validation in Qingchuan County demonstrated that the proposed model improved the capture rate for high/very high susceptibility areas by 48.86%, improving it from 4.55% to 53.41%, with a site density of 0.0469 events/km² in very high-susceptibility zones. Overall, this framework offers a high-precision and interpretable debris-flow risk management tool, highlights the substantial influence of anthropogenic factors such as roads and land development, and introduces a “negative-sample screening with cross-regional generalization” strategy to support land-use planning and disaster prevention in mountainous regions. Full article

Gully development is a significant geomorphological and environmental process that affects land degradation worldwide, with ephemeral gullies (EGs) and permanent gullies (PGs) being the two most common types. These two gully types are often spatially connected, and with such EG-PG connectivity can accelerate erosion. However, systematic research on this phenomenon remains limited, particularly at the regional scale. This study focuses on the spatial connectivity between EGs and PGs in the Songnen black soil region of northeast China. An unequal probability stratified sampling was used to establish 977 small watershed units, and a database of gullies and their connectivity was constructed based on sub-meter imagery. Among them, 55 representative units were randomly selected within geomorphic zones for field surveys and UAV validation to ensure data accuracy. Spatial patterns of gully connectivity were analyzed, and dominant controlling factors were identified using the Geodetector, which quantifies spatial stratified heterogeneity and evaluates the explanatory power of potential driving factors. The results are as follows: (1) Gully connectivity varies significantly across the region, with hotspot areas where more than 50% of permanent gullies are connected to ephemeral gullies, and cold spot clusters elsewhere. (2) Permanent gullies connected to ephemeral gullies differ significantly from unconnected ones in both length and width, with the former exhibiting a more elongated morphology. (3) Slope length and mean annual precipitation are the primary drivers of gully connectivity, both showing significant positive effects. Moreover, the interaction between mean annual precipitation and slope length shows the strongest explanatory power, indicating that precipitation, in combination with topographic features, plays a dominant role in shaping gully connectivity. By examining the spatial patterns of gully connectivity, this study contributes to a more refined understanding of gully morphological evolution and offers empirical insights for enhancing gully erosion models and optimizing regional soil and water conservation strategies. Full article

This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article

Accurate surface wind speed data are vital for atmospheric science, climatology, and energy applications. European Centre for Medium-Range Weather Forecasts Reanalysis v.5 (ERA5), as one of the most widely used global reanalysis datasets, has insufficient assessment of its applicability across diverse landform types. Using the gridded observational dataset over China (CN05.1) and the Global Basic Landform Units dataset, this study evaluated the surface wind speed data from ERA5 over various altitudinal zones and undulating terrains in China via root-mean-square differences (RMSD) and mean absolute percentage error (MAPE) against CN05.1 observations. Results reveal significant regional variations, with ERA5 effectively capturing the spatial distribution of mean wind speeds but systematically underestimating magnitudes, particularly in plateau and mountainous regions. ERA5 reanalysis fails to reproduce the observed altitudinal increase in surface wind speed. Elevation-dependent biases are prominent, with RMSD and MAPE increasing from low-altitude to high-altitude areas. Terrain complexity exacerbates errors, showing maximum deviations in high-relief mountains and minimum deviations in hilly regions. These biases evolve seasonally, peaking in spring and reaching minima in winter. In summary, discrepancies between observations and ERA5 vary with altitude, topographic relief, and season. The most significant deviations occur for spring surface winds in high-altitude, high-relief mountains, with mean RMSD reaching 3.3 m/s and MAPE 553%. The findings highlight the limitations of ERA5 reanalysis data in scientific and operational contexts over complex terrains. Full article

Digital elevation models (DEMs), as the fundamental unit of terrain morphology, are crucial for understanding surface processes and for land use planning. However, automated classification faces challenges due to inefficient terrain feature extraction from raw LiDAR point clouds and the limitations of traditional methods in capturing fine-scale topographic variations. To address this, we propose a novel hybrid RNN-CNN framework that integrates multi-scale Topographic Position Index (TPI) features to enhance DEM generation. Our approach first models voxelated LiDAR point clouds as spatially ordered sequences, using Recurrent Neural Networks (RNNs) to encode vertical elevation dependencies and Convolutional Neural Networks (CNNs) to extract planar spatial features. By incorporating TPI as a semantic constraint, the model learns to distinguish terrain structures at multiple scales. Residual connections refine feature representations to preserve micro-topographic details during DEM reconstruction. Extensive experiments in the complex terrains of Jiuzhaigou, China, demonstrate that our lightweight hybrid framework not only achieves excellent DEM reconstruction accuracy in complex terrains, but also improves computational efficiency by more than 20% on average compared to traditional interpolation methods, making it highly suitable for resource-constrained applications. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

We inspected a sector of the Apennines (central–southern Italy) in geographic and structural continuity with the Quaternary-active extensional belt but where clear geomorphic and seismological signatures of normal faulting are unexpectedly missing. The evidence of active tectonics in this area, between Abruzzo and Molise, does not align with geodetic deformation data and the seismotectonic setting of the central Apennines. To investigate the apparent disconnection between active deformation and the absence of surface faulting in a sector where high lithologic erodibility and landslide susceptibility may hide its structural evidence, we combined multi-scale and multi-source data analyses encompassing morphometric analysis and remote sensing techniques. We utilised high-resolution topographic data to analyse the topographic pattern and investigate potential imbalances between tectonics and erosion. Additionally, we employed aerial-photo interpretation to examine the spatial distribution of morphological features and slope instabilities which are often linked to active faulting. To discern potential biases arising from non-tectonic (slope-related) signals, we analysed InSAR data in key sectors across the study area, including carbonate ridges and foredeep-derived Molise Units for comparison. The topographic analysis highlighted topographic disequilibrium conditions across the study area, and aerial-image interpretation revealed morphologic features offset by structural lineaments. The interferometric analysis confirmed a significant role of gravitational movements in denudating some fault planes while highlighting a clustered spatial pattern of hillslope instabilities. In this context, these instabilities can be considered a proxy for the control exerted by tectonic structures. All findings converge on the identification of an ~20 km long corridor, the Castel di Sangro–Rionero Sannitico alignment (CaS-RS), which exhibits varied evidence of deformation attributable to active normal faulting. The latter manifests through subtle and diffuse deformation controlled by a thick tectonic nappe made up of poorly cohesive lithologies. Overall, our findings suggest that the CaS-RS bridges the structural gap between the Mt Porrara–Mt Pizzalto–Mt Rotella and North Matese fault systems, potentially accounting for some of the deformation recorded in the sector. Our approach contributes to bridging the information gap in this complex sector of the Apennines, offering original insights for future investigations and seismic hazard assessment in the region. Full article