Search Results (483)

Flooding is a destructive natural hazard intensified by climate change, posing challenges to sustainable disaster risk management. This study developed and evaluated machine learning models for flood severity prediction within a hexagonal grid system (H3, resolution 8) under rainy season conditions in Maha Sarakham, Thailand. Four models Random Forest (RF), XGBoost, Gradient Boosting (GB), and Support Vector Machine (SVM) were trained using 11 environmental variables across historical years (2018, 2021, 2022) and tested on a projected year (2025) under SSP scenarios. XGBoost demonstrated the most stable performance (accuracy > 0.95 across all years), while SVM achieved high historical accuracy (0.970 average) but failed to detect positive flood cases in 2025 (recall = 0), highlighting the importance of temporal validation. Topographic variables were the most consistent predictors, but NSMI (soil moisture) emerged as the top SHAP predictor in 2025 (r = 0.52), suggesting a shift in flood-generating mechanisms under climate change. A polarization pattern was observed: flood-affected area declined to 6.8% in 2025 (79% reduction from 2022), yet maximum flood point counts remained high at 14.0, indicating more concentrated but intense flooding. Under SSP projections, using the historical baseline (27.5%), SSP1-2.6 (45.2%) and SSP2-4.5 (45.0%) indicate increased flood risk relative to the historical baseline through 2040. The SSP5-8.5 projection (3.4%) is identified as a model extrapolation artifact through formal out-of-distribution assessment (Mahalanobis distance = 8.72, p < 0.001) and is therefore excluded from policy recommendations. Although GRU and LSTM achieved marginally higher AUC values in retrospective validation, we recommend XGBoost for operational forecasting due to its temporal stability, computational efficiency, and interpretability. We further recommend integrating real-time soil moisture monitoring into early warning systems and shifting to hotspot-targeted adaptation strategies. Full article

In order to mitigate the negative effects of heavy rainfall events, natural water retention measures (NWRM)—such as hedges, erosion control strips, vegetated drainage channels, wooded strips, retention basins and ditch pockets—have gained renewed attention as an effective climate adaptation strategy. To identify potential areas for NWRM, this study applied and methodologically expanded an existing approach for identifying dominant runoff processes (DRPs) to an agricultural sub-catchment in the Weserbergland region of Germany. The DRP were determined using a Geographic Information System (GIS) and validated through field surveys. Potential areas for water retention within the same runoff process classes were identified for three defined objectives: improving infiltration, extending flow paths, and redirecting runoff to surrounding areas. Spatial differentiation was achieved using accumulated catchment area and overland flow distance. The watershed is predominantly characterized by surface runoff (Hortonian Overland Flow). Field validation confirmed the DRP classification for around two-thirds of the study area, with deviations occurring predominantly on arable land. Supplementing the DRP approach with a topographic analysis allowed for further differentiation, focusing on small, topographically defined sub-watersheds. The identified areas offer significant potential for interventions. Combined with supplementary data, analyses of the water network and the involvement of local stakeholders, the resulting potential map provides a solid basis for planning smaller-scale water retention measures. Full article

Flood susceptibility maps are essential tools for identifying high-risk areas. However, traditional approaches often face limitations in spatial resolution and adaptability under changing climatic conditions, particularly in data-scarce regions. This study addresses these limitations through a data-driven geospatial approach that integrates high-resolution Earth Observation and Geographic Information Systems (GIS) data to improve flood susceptibility assessment in a small river basin in Romania. Ten flood conditioning factors were analyzed, including Elevation, Slope, Topographic Wetness Index (TWI), Topographic Position Index (TPI), Profile Curvature, Aspect, Soil Texture, Distance to the River, Normalized Difference Vegetation Index (NDVI), and Soil Moisture. Historical flood extent data extracted from PlanetScope imagery were used for model training and validation. Two statistical methods, Frequency Ratio (FR) and Weight of Evidence (WoE), were applied to map flood susceptibility at a 12.5 m resolution. Results indicate that both models captured the spatial variability of flood-prone areas, but WoE achieved higher predictive performance (AUC = 0.945) than FR (AUC = 0.876), while FR tended to underestimate flood-prone zones. Half of the basin falls within low to very low susceptibility classes, whereas high and very high susceptibility together occupy about 25–29% of the basin and concentrate along river corridors in the central and southern sectors, overlapping with built-up areas. Consequently, about 38% (WoE) and 30% (FR) of the total built-up area fall within high and very high susceptibility classes. The results demonstrate that integrating high-resolution open-source Earth Observation data with statistical modeling provides a reliable, transferable framework for flood susceptibility assessment and land-use planning in data-scarce environments. 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

Accurate estimation of depth–duration–frequency (DDF) curves is essential for hydrological analyses and flood-risk mitigation. Regionalization methods are particularly important in areas with limited observations, but their performance may deteriorate where topography and climate generate strong spatial variability. This study investigates the Gargano promontory in southern Italy, an area that is commonly treated as a single hydrologically homogeneous zone despite its marked morphological and climatic contrasts. Annual maximum rainfall data from eight rain gauges, together with topographic and climatic descriptors, were analyzed to assess whether local factors help explain the variability of DDF-curve parameters. The analysis focused on elevation, distance from the sea, and a wind-effect index derived from a digital elevation model. The results indicate that the regional behavior of extreme rainfall is not spatially uniform and that several DDF-curve parameters are influenced by local physiographic controls. Different controls emerge for different station groups, and a physiographically based subdivision of the Gargano promontory into windward and leeward sectors provides a more coherent representation of DDF-curve parameters than the currently adopted single-zone regionalization. Full article

Floods are among the most destructive natural hazards, posing a significant risk to lives and infrastructure worldwide. Effective flood risk management demands precise, multidimensional approaches. In this direction, the current study aims to evaluate and compare two methods for flood-risk assessment: the scenario-based Ordered Weighted Averaging (OWA) and the data-driven Random Forest (RF) method. To this end, the Great Karun watershed in Iran was chosen due to its complex hydrological and climatic conditions. Hydro-climatic, hydrological, topographic, land-cover datasets, and actual flood observations were applied and analyzed based on fifteen influencing factors recommended by expert opinion. In the OWA approach, while factor weights were determined using the Best-Worst Method, flood-risk maps were produced based on five scenarios: very optimistic, optimistic, intermediate, pessimistic, and very pessimistic. In the RF approach, factor importance index was calculated via the mean decrease impurity algorithm, and the model was trained to generate flood-risk maps. Results showed that distance from rivers and slope were the most influential factors in OWA, while precipitation and flow accumulation dominated in RF. Prediction rate for OWA scenarios ranged from 1.4 to 3.5%, while RF achieved 16.0%, and the Area Under the Curve (AUC) was 0.984. Optimistic scenarios overestimated, and pessimistic scenarios underestimated risk, with the OWA intermediate scenario most closely matching RF results. RF demonstrated superior performance for flood-risk classification, highlighting its applicability for precise flood management. Overall, this study presents a novel comparative framework by integrating scenario-based OWA-BWM and Random Forest approaches to investigate the effects of decision-maker preferences and data-driven learning on flood susceptibility mapping. Full article

The Yarlung Zangbo River region is characterized by complex geological structures and distinctive physiographic environments, where landslide deposits are susceptible to deformation and instability under internal and external forces. Therefore, determining their potential movement processes, kinematic characteristics, and failure modes is crucial for landslide hazard prevention and mitigation. Taking the Yuqiong landslide deposit as a case study, this paper employed field investigation, laboratory testing, and numerical simulation to model its instability and failure process, as well as analyze its movement characteristics and the failure mechanism. The main results are as follows: (1) The entire sliding process of landslide instability and failure lasts approximately 100 s. The deformation and instability process can be divided into four stages: initiation and sliding, deformation propagation, deformation accumulation, and cessation. The velocity evolution comprises three stages: start-up acceleration (accounting for 10%), rapid deceleration and slow deformation (together accounting for 90%). The initiation of the crown exhibits a certain degree of suddenness. (2) The energy distribution during landslide instability is controlled by topographic slope, sliding mass thickness, and travel distance. At the crown, where the slope is steep, the thickness is large, and the travel distance is short, frictional energy dissipation is low, resulting in concentrated energy. In contrast, at the toe and the middle part, where the slopes are gentle and the travel distances are long, energy dissipation is high, leading to lower energy distribution. (3) The deformation and failure mechanism of the landslide is characterized as follows: active thrusting at the crown drives the movement; the toe, owing to the gentle slope and thick layer that provide high sliding resistance, undergoes slow retrogressive buffering; the middle part is subjected to both pushing and pulling, resulting in settlement and stress transfer. Overall, the landslide exhibits a composite progressive failure mode combining crown thrusting and toe retrogressive action. Full article

Wildfires threaten natural ecosystems and human livelihoods in the Tanzanian Miombo woodlands. This study presents the first locally calibrated, high-resolution wildfire susceptibility map for the Iringa region, developed using a robust machine learning framework. Multi-decadal remote sensing data (MODIS fire occurrences, 2001–2024) were integrated with climatic, topographic, vegetation, and anthropogenic variables to train four classifiers: Random Forest, XGBoost, support vector machine with RBF kernel, and Logistic Regression. A balanced dataset of 9096 fire points and an equal number of randomly sampled non-fire points was used. The data were split into 70% for training and 30% for testing. Model performance was evaluated using accuracy, area under the ROC curve (AUC), accuracy, precision, and F1-score. Random Forest achieved the highest overall performance (AUC = 0.845, accuracy = 0.759, precision = 0.789 and F1 = 0.771), followed by XGBoost (AUC = 0.828, accuracy = 0.736, precision = 0.700 and F1 = 0.757), SVM (AUC = 0.755, accuracy = 0.679, precision = 0.648 and F1 = 0.709), and Logistic Regression (AUC = 0.740, accuracy = 0.661, precision = 0.631 and F1 = 0.696). Feature importance analysis identified altitude as the most influential variable, followed by wind speed, distance to road, and NDVI. Kernel Density Estimation revealed spatially distinct fire clusters concentrated in central and southern hotspots. Temporal analysis showed that 94% of fires occur during the dry season (June–November), peaking sharply in October. These findings provide an evidence-based framework for fire prevention and sustainable management of Iringa’s Miombo woodlands. Full article

Landslides are among the most destructive geological hazards, posing significant threats to human life, infrastructure, and ecological environments. In this research, to improve the accuracy and reliability of landslide susceptibility assessment, Guangdong Province was selected as the study area, and a multi-source environmental factor dataset incorporating topographic, geological, hydrological, climatic, vegetation, and anthropogenic factors was constructed. Geological factors, including fault distance and seismic point distance, were introduced to characterize the influence of tectonic activities on slope instability. A landslide inventory and a non-landslide sample dataset were established for model training and validation. The Extreme Gradient Boosting (XGBoost) model was employed for landslide susceptibility mapping, and SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of different conditioning factors. The results showed that the model achieved an area under the receiver operating characteristic curve (AUC) of 0.8335 on the independent test dataset and a mean AUC of 0.8457 ± 0.0219 for a five-fold stratified cross-validation. The high-susceptibility areas were primarily distributed in the mountainous and hilly regions of northern and eastern Guangdong Province. Vegetation-related variables, road proximity, land-cover type, slope, and distance to coal mines were identified as important contributors to landslide occurrence. This study provides useful references for geological hazard prevention, risk management, and sustainable regional planning. Full article

With the continuing advancement of global environmental change and rapid urbanization, rural human settlements are facing multiple pressures, including ecological degradation, spatial decline, population outflow, and functional weakening. Based on the vulnerability analysis framework, studies on rural vulnerability provide an important perspective for assessing villages’ risk exposure, disturbance response, and functional degradation when coping with internal and external disturbances. However, existing studies often rely on single-dimensional or linearly weighted evaluations, making it difficult to comprehensively reveal the coupling relationships among multiple discriminative variables and the spatial differentiation patterns of vulnerability. Taking rural areas in Hancheng City, Shaanxi Province, as the research object, this study selects 12 indicators from three dimensions—natural ecological constraints, settlement spatial organization, and public service support—to provide proxy representations of conditions related to potential rural vulnerability. K-means clustering was used to identify potential vulnerability configuration types under multidimensional indicator combinations. A Python-based XGBoost model was then employed as an interpretable surrogate model to assist in characterizing the clustering boundaries, while SHAP analysis was used to explain the key discriminative variables associated with type membership. The results show that the potential rural vulnerability configurations in Hancheng City present a significant west–central–east spatial differentiation pattern. Elevation, village core density, topographic wetness index, distance to town centers, accessibility of daily service facilities, distance to major roads, and normalized difference vegetation index are the main discriminative variables distinguishing different potential vulnerability configuration types. Among them, village core density shows a particularly strong explanatory role. Different key discriminative variables also exhibit evident nonlinear response characteristics across different potential types. Under the indicator system and the $K$ = 4 clustering scheme adopted in this study, the potential rural vulnerability configurations in Hancheng City can be summarized into four types: service-concentrated settlement type, complex terrain-constrained type, human–land coupling transitional type, and natural ecological isolation type. The findings reveal the spatial differentiation characteristics, variable combination relationships, and typological discriminative features of potential rural vulnerability configurations in Hancheng City. They can provide a case-based reference for identifying potential vulnerability, conducting spatial zoning diagnosis, and supporting classified governance in similar county-level rural areas within the loess hilly and gully region. In practical terms, this framework can serve as a diagnostic tool for local governments and planners in classified rural governance. It can be used to identify priority areas for public service and infrastructure investment, review key risk-control areas in complex terrain zones, delineate low-intensity use and protection boundaries in ecologically isolated areas, and guide differentiated resource allocation for different types of villages. Full article

Accurately estimating village-level winter wheat yield in coastal saline–alkali farmland is challenging because this region has strong spatial differences and multiple environmental stresses. In this study, Huanghua City, Hebei Province, was selected as a typical coastal saline–alkali area. Sentinel-2 images, climate factors, and topographic variables, including elevation, topographic wetness index, distance to the coastline, and distance to water systems, were combined to build a phenology-guided feature set for winter wheat yield prediction in coastal areas. The results showed that Phenology-Guided Feature Integration XGBoost achieved an R² of 0.6382 and an RMSE of 450.15 kg/ha, which was slightly better than Gradient Boosting (R² = 0.6256) and Random Forest (R² = 0.6098), and clearly better than SVR (R² = 0.4792), Ridge regression (R² = 0.4582), and a single Decision Tree (R² = 0.3088). Then, a three-stage branch was designed to identify the main drivers of SI, NDVI, and winter wheat yield at different stages, helping explain how environmental constraints and vegetation responses jointly affect final yield. The Three-Stage Fusion XGBoost Model achieved an R² of 0.6439, an RMSE of 446.24 kg/ha, and an MAE of 363.38 kg/ha, showing a slight improvement in prediction accuracy. SHAP analysis showed that SI, distance-related factors, elevation, TWI, and NDVI were important drivers of winter wheat yield variation. Spatial prediction results showed higher winter wheat yield in inland areas (5145 kg/ha) and lower yield in coastal areas (4198 kg/ha). This framework supports village-scale winter wheat yield prediction in coastal saline–alkali farmland and improves model interpretability. Full article

Coastal dune ecosystems of the Moroccan Atlantic coast are among the most threatened environments of the western Mediterranean basin, yet the ecological preferences of their constituent psammophilous flora remain poorly documented. Using the ecological profile method, corrected frequencies, species and descriptor entropy, mutual information and ecological barycentres were calculated for three environmental descriptors, distance to the tidal fluctuation zone, topographic position and soil organic matter content, applied to a presence–absence matrix of 53 vascular taxa across 123 plots distributed among three dune facies. Of the total taxa inventoried, 41 were retained as ecologically active. Topography and soil organic matter emerged as the most efficient descriptors, both exceeding the 5% activity threshold. Cross-referencing of ecological groups identified three phytoecological assemblages: characteristic psammophytes of embryonic dunes, species of the primary foredune and species of the enriched backdune. The dominance of intermediate ecological amplitudes and the convergence of introduced species towards organic matter-rich backdune conditions signal chronic anthropogenic disturbance. This study provides a quantitative characterisation of psammophilous species ecological preferences and phytoecological assemblages along the coast–inland gradient of the Haouzia Bay SBEI, constituting an operational reference framework for targeted conservation and restoration management of this threatened coastal site. Full article

Global climate warming has intensified in recent years, with extreme weather events occurring more frequently and severely impacting ecosystems and social production. According to the “China Climate Change Blue Book (2023),” China’s temperature rise rate exceeds the global average, with increasingly significant impacts on ecosystems. Hyphantria cunea, an invasive forest pest first discovered in China in 1979, has spread widely, causing serious damage to forestry and agriculture and posing a significant threat to China’s ecological security. To address this threat, this study employed seven modeling algorithms (GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt) from the R Biomod2 package to develop an ensemble model. The core research objective of this work is to quantify climate-driven range shifts of H. cunea under ongoing global climate change. Previous nationwide SDM studies on invasive forest pests have consistently demonstrated that climatic variables dominate broad-scale nationwide suitable habitat patterns at the macro-regional level. Supplementary topographic, vegetation cover, and human land-use disturbance layers were incorporated to capture fine-scale habitat filtering effects and long-distance pest dispersal facilitated by human activities, which together fully characterize the suitable regional environments of this pest. By integrating climate, topography, vegetation, and human disturbance data, we predicted the potential geographical distribution of H. cunea in China under four future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The ensemble model achieved excellent performance with TSS and ROC values of 0.901 and 0.984, respectively. Currently, highly suitable areas for H. cunea are concentrated in 12 provinces, including Shandong, Jiangsu, Hebei, Henan, and Anhui, covering 56.33 × 104 km², with Shandong showing the highest proportion (25.48%). The suitable habitat range is projected to expand northeastward, with significant increases under high emission scenarios (SSP5-8.5). Analysis of environmental variables reveals that nighttime light brightness, precipitation in the warmest season, the seasonal temperature variation coefficient, and average temperature in the driest season are key factors influencing H. cunea distribution. Nighttime light brightness shows the highest contribution (27.7%), indicating significant human impact on species spread. Response curves suggest that H. cunea favors warm, humid areas with pronounced seasonal changes. This study demonstrates that climate change will increase H. cunea expansion risk, necessitating strengthened cross-regional monitoring and biological control techniques. These findings provide a scientific foundation for understanding H. cunea spatiotemporal distribution patterns under future climate scenarios and for developing effective prevention and control strategies. Full article

Rapid and reliable landslide mapping from satellite observations is essential for hazard assessment, emergency response, and reservoir-area risk management, yet automatic segmentation remains challenging in mountainous regions because landslide scars are spectrally heterogeneous, terrain-constrained, morphologically irregular, and frequently confused with other exposed surfaces. This study proposes GeoSlide-XMamba, a terrain-conditioned spectral-topographic boundary-aware state-space network for pixel-wise landslide semantic segmentation. The model first separates Sentinel-2 spectral bands and DEM/slope-derived topographic layers into modality-specific branches, integrates them through spectral-topographic adaptive fusion (STAF++), and then performs terrain-conditioned selective state-space scanning in the XMamba bottleneck. Unlike direct token concatenation, the proposed bottleneck uses terrain descriptors to dynamically weight directional selective scan branches so that long-range feature propagation is guided by slope-related morphology. Boundary-aware decoding, signed-distance supervision, and hard-negative mining are further introduced to improve inventory-oriented geometric quality and suppress common false positives. Experiments were conducted on the Landslide4Sense benchmark using 14-channel multispectral-topographic inputs. Among the compared methods, GeoSlide-XMamba achieved the highest validation performance under a unified five-seed protocol, with precision = 0.729, recall = 0.626, F1-score = 0.673, IoU = 0.507, kappa = 0.666, Boundary-F1 = 0.466, and HD95 = 3.45 pixels. Five-seed experiments produced F1 = 0.673 ± 0.003, IoU = 0.507 ± 0.002, Boundary-F1 = 0.466 ± 0.002, and HD95 = 3.45 ± 0.13 pixels, with a 95% CI of [0.670, 0.676] for F1. Relative to the strong 14-channel concatenation baseline, the proposed model improves mean F1 by 0.045 and reduces HD95 by 1.42 pixels. Expanded qualitative inference on Jinsha River patches indicates that the learned spectral-topographic representation transfers plausibly to high-relief reservoir-canyon terrain. These results show that terrain-conditioned state-space modeling can improve both segmentation accuracy and boundary geometry for remote sensing landslide mapping. Full article

Climate change is increasingly reshaping species habitat suitability worldwide. Primula L., the largest genus in Primulaceae, comprises 404 species in China (including 296 endemic species) and is characterized by high endemism and numerous rare and endangered taxa. However, global warming has intensified habitat fragmentation and loss, while its distribution patterns and key environmental drivers remain insufficiently understood. We compiled 7647 occurrence records of 404 wild Primula species in China and integrated 60 environmental variables (climatic, topographic, and soil factors). Using the MaxEnt model combined with ArcGIS spatial analysis, we assessed current and future habitat suitability, identified dominant environmental drivers, and quantified conservation gaps under multiple climate scenarios. Species richness is highly concentrated in Sichuan (186 species), Yunnan (177 species), and Xizang (165 species), with the Hengduan Mountains and eastern Himalayas representing the core distribution area and showing clear peripheral differentiation. The optimized MaxEnt model performed well (AUC = 0.858), identifying temperature seasonality (bio4, 39.8%) and elevation (27.1%) as the main limiting factors. The total suitable habitat area is 268.52 × 10⁴ km², with high-suitability areas mainly distributed in the Hengduan Mountains, southeastern Qinghai–Xizang Plateau, and the Central Mountain Range of Taiwan. Under three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245, and SSP585), suitable habitat shows a persistent decline, most pronounced under SSP585 in the 2090s (−20.73%), accompanied by a 25.86% reduction in low-suitability areas. Localized expansion of high-suitability habitats suggests that the Hengduan Mountains and southeastern Qinghai–Xizang Plateau may act as potential climatic refugia. Habitat loss consistently exceeds habitat gain, while the distribution centroid shifts westward and northwestward, with migration distances increasing under higher-emission scenarios. Conservation gap analysis indicates that 90.01% of high-suitability habitats lie outside the current protected area network, revealing a strong mismatch between biodiversity hotspots and conservation coverage. These findings highlight the urgent need to expand protected areas and establish micro-reserves in key gap regions (southwestern Sichuan, northwestern Yunnan, southeastern Xizang, and southern Gansu), and to integrate climate-driven migration corridors into conservation planning to support long-term alpine plant persistence under climate change. Full article