Search Results (48)

The formation and development of gullies are pervasive drivers of hillslope degradation, yet forecasting where and at what elevation gullies begin remains challenging. This study proposes a morphometric–energetic framework to anticipate gully-initiation zones in catchments developed on low-permeability lithologies and limited tectonic control across contrasting climatic and geomorphic settings. Using GIS analyses and morphometric parameters, with some derived from hypsometric curves, our objective is to link basin-scale morphology and energy distribution to the propensity for linear incision, thereby defining a statistically representative initiation belt and stream network positions most susceptible to gully initiation. The study results show that the altitudinal range most susceptible to gully development is at the mean basin’s elevation, and that this range can be associated with an energy potential (Şen’s “Energy Index”) similar to those used to calculate hydroelectric potential in a river basin. Furthermore, the study highlights that the contributing area required to activate these erosive processes varies within fairly narrow limits, between 1 and 3 ha. The framework is designed to be quantitative, transferable among landscapes, and parsimonious in data requirements, even if applicable, as mentioned, in basins with low-permeability lithology and limited tectonic control, and as a first-level predictive tool. By prioritizing diagnostics that can be computed from standard topographic datasets, the approach aims to support land-use planning and sediment-risk mitigation, offering a practical pathway for early identification and management of areas vulnerable to gullying. Full article

Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95, $\kappa$ = 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management. Full article

Soil piping is the process by which subsurface water creates and enlarges channels, or “pipes,” within soil, enabling rapid and preferential flow beneath the surface. The collapse of these eroded pipes can lead to land degradation, gully formation, and potential damage to overlying infrastructure. While the structural consequences of pipe collapse are well recognized, there is limited understanding of the factors controlling pipe collapse and how water within the pipe influences moisture levels within a slope. This study used physical models of unsaturated slopes to examine how compaction conditions, pipe characteristics, and hydraulic conditions affect the progression of internal erosion. Models were created with different initial pipe sizes, moisture contents, densities at compaction and levels of pipe connectivity. Volumetric water content (VWC) sensors and cameras were used to monitor the slope response to subsurface flow, and measurements of pipe geometry were collected after the tests. Results showed that lower initial soil water content was more susceptible to pipe collapse, while higher water content showed improved pipe stability and sustained preferential flow. Fully connected pipes grew through erosion due to the pipe flow, while disconnected pipes grew mainly through local pipe collapse. Hydraulic equilibrium and soil erodibility affected the final pipe morphology more than the initial pipe size. These experimental results demonstrate that soil fabric and hydraulic connectivity of the pipe control the progression of piping, likelihood of collapse, and movement of water within the soil matrix. Full article

Engineered loess-filled gullies, which are widely distributed across China’s Loess Plateau, face significant stability challenges under extreme rainfall conditions. To elucidate the regulatory mechanisms of antecedent rainfall on the erosion and failure processes of such gullies, this study conducted large-scale flume experiments to reveal their phased erosion mechanisms and hydromechanical responses under different antecedent rainfall durations (10, 20, and 30 min). The results indicate that the erosion process features three prominent phases: initial splash erosion, structural reorganization during the intermission period, and runoff-induced gully erosion. Our critical advancement is the identification of antecedent rainfall duration as the primary “pre-regulation” factor: short-duration (10–20 min) rainfall predominantly induces surface crack networks during the intermission, whereas long-duration (30 min) rainfall directly triggers substantial holistic collapse. These differentiated structural weakening pathways are governed by the duration of antecedent rainfall and fundamentally control the initiation thresholds, progression rates, and channel morphology of subsequent runoff erosion. The long-duration group demonstrated accelerated erosion rates and greater erosion amounts. Concurrent monitoring demonstrated that transient pulse-like increases in pore-water pressure were strongly coupled with localized instability and gully wall failures, verifying the hydromechanical coupling mechanism during the failure process. These results quantitatively demonstrate the critical modulatory role of antecedent rainfall duration in determining erosion patterns in engineered disturbed loess, transcending the prior understanding that emphasized only the contributions of rainfall intensity or runoff. They offer a direct mechanistic basis for explaining the spatiotemporal heterogeneity of erosion and failure observed in field investigations of the engineered fills. The results directly contribute to risk assessments for land reclamation projects on the Loess Plateau, underscoring the importance of incorporating antecedent rainfall history into stability analyses and drainage designs. This study provides essential scientific evidence for advancing the precision of disaster prediction models and enhancing the efficacy of mitigation strategies. Full article

Gully erosion is one of the significant environmental issues facing the black soil regions in Northeast China, and its formation is closely related to various environmental factors. This study employs multiple machine learning models to assess gully erosion susceptibility in this region. The primary objective is to evaluate and optimize the top-performing model under high-resolution UAV data conditions, utilize the optimized best model to identify key factors influencing the occurrence of gully erosion from 11 variables, and generate a local gully erosion susceptibility map. Using 0.2 m resolution DEM and DOM data obtained from high-resolution UAVs, 2,554,138 pixels from 64 gully and 64 non-gully plots were analyzed and compiled into the research dataset. Twelve models, including Logistic Regression, K-Nearest Neighbors, Classification and Regression Trees, Random Forest, Boosted Regression Trees, Adaptive Boosting, Extreme Gradient Boosting, an Artificial Neural Network, a Convolutional Neural Network, as well as optimized XGBOOST, a CNN with a Multi-Head Attention mechanism, and an ANN with a Multi-Head Attention Mechanism, were utilized to evaluate gully erosion susceptibility in the Dahewan area. The performance of each model was evaluated using ROC curves, and the model fitting performance and robustness were validated through Accuracy and Cohen’s Kappa statistics, as well as RMSE and MAE indicators. The optimized XGBOOST model achieved the highest performance with an AUC-ROC of 0.9909, and through SHAP analysis, we identified roughness as the most significant factor affecting local gully erosion, with a relative importance of 0.277195. Additionally, the Gully Erosion Susceptibility Map generated by the optimized XGBOOST model illustrated the distribution of local gully erosion risks. Full article

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts. Full article

Soil erosion is a global issue—with gully erosion recognized as one of the most important forms of land degradation. The purpose of this study is to compare and contrast the outcomes of four machine learning models, Classification and Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), used for mapping susceptibility to soil gully erosion. The controlling factors of gully erosion in the Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation in Google Earth and gully erosion samples (n = 159) were used for modelling and spatial prediction. The XGBoost and RF models achieved identical results for the area under the receiver operating characteristic curve (AUROC = 88.50%), followed by the SVM and CART models, respectively (AUROC = 86.17%; AUROC = 85.11%). In all models analysed, the importance of the main controlling factors predominated among Lineaments, Land Use and Cover, Slope, Elevation and Rainfall, highlighting the need to understand the landscape. The XGBoost model, considering a smaller number of false negatives in spatial prediction, was considered the most appropriate, compared to the Random Forest model. It is noteworthy that the XGBoost model made it possible to validate the hypothesis of the study area, for susceptibility to gully erosion and identifying that 9.47% of the Piraí Drainage Basin is susceptible to gully erosion. Furthermore, replicable methodologies are evidenced by their rapid applicability at different scales. Full article

Benggang erosion is one of the most severe geomorphological hazards occurring on deeply weathered crusts in the hilly regions of southern China. Unraveling the susceptibility and pinpointing the risk areas of Benggang erosion are essential for developing effective prevention and management strategies. This study introduced the Maxent model to investigate Benggang erosion susceptibility (BES) and compared the evaluation results with the widely used Random Forest (RF) model. The findings are as follows: (1) the incidence of Benggang erosion is rising initially with an increase in elevation, slope, topographic wetness index, rainfall erosivity, and fractional vegetation cover, followed by a subsequent decline, highlighting its distinct characteristics compared to typical types of gully erosion; (2) the AUC values from the ROC curves for the Maxent and RF models are 0.885 and 0.927, respectively. Both models converge on elevation, fractional vegetation cover, rainfall erosivity, Lithology, and topographic wetness index as the most impactful variables; (3) both models adeptly identified regions prone to potential Benggang erosion. However, the Maxent model demonstrated superior spatial correlation in its susceptibility assessment, contrasting with the RF model, which tended to overestimate the BES in certain regions; (4) the Maxent model’s advantages include no need for absence samples, direct handling of categorical data, and more convincing results, suggesting its potential for widespread application in the BES assessment. This research contributes empirical evidence to study the Benggang erosion developing conditions in the hilly regions of southern China and provides an important consideration for the sustainability of the regional ecological environment and human society. Full article

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices. Full article

The seasonal Xiangka debris flow, breaking out frequently in Xinghai County, Qinghai Province, poses a serious threat to resident safety, has significant potential economic impacts, and inflicts severe damage on the geological environment, vegetation, and land resources in the area. Therefore, a susceptibility assessment is crucial. Utilizing data from field investigations, meteorology, and remote sensing, this study devised an assessment system using 10 evaluation factors with pronounced regional characteristics as susceptibility indices. Based on data processing using ArcGIS 10.7 and MATLAB R2016B, this study assessed the susceptibility of the Xiangka debris flow using AHP, the fuzzy comprehensive evaluation method, and a cloud model. The analysis results show that, based on AHP, the primary index affecting the occurrence of Xiangka debris flow is mainly source factor (0.447). The secondary indices are mainly the length ratio of the mud sand supply section (0.219), fractional vegetation cover (FVC, 0.208), and watershed area (0.192). Combined with the actual characteristics, it can be seen that the formation conditions of the Xiangka debris flow primarily encompass the following: sources such as slope erosion and accumulation at gully exits, challenging topography and terrain conducive to the accumulation of water and solid materials, and water source aspects like surface runoff from intense rainfall. Based on the fuzzy mathematical method—fuzzy coordinate method—cloud model, it is concluded that the degree of susceptibility is mild-to-moderate. The combination of these methods provides a new idea for the evaluation of debris flow susceptibility. This study can provide a theoretical basis for the layout of treatment engineering and geological disaster prevention in this area and promote the sustainable development of the ecological environment. Full article

Gully erosion poses severe problems for land degradation in several areas worldwide. This study aims to evaluate the accuracy and robustness of the maximum entropy (MaxEnt) method for assessing gully erosion susceptibility. We selected the catchment of the Mesima stream as the test site, which is situated in the southwest sector of the Calabria region (South Italy). An inventory map of gully erosion was realised and 12 predisposing factors, such as lithology, soil texture, soil bulk density, land use, drainage network, slope gradient, aspect, length–slope (LS), plan curvature, stream power index (SPI), topographic position index (TPI), and topographic wetness index (TWI), were selected to implement the dataset in the MaxEnt method. The accuracy and uncertainty of the method were tested by 10-fold cross-validation based on accuracy, kappa coefficient, and receiver operating characteristic curve (ROC) and related area under curve (AUC). The dataset was randomly divided into 10 equal-sized groups (folds). Nine folds (90% of the selected dataset) were used to train the model. Instead, the remaining fold (10% of the dataset) was used for testing the model. This process was repeated 10 times (equal to the number of the folds) and each fold was used only once as the validation data. The average of 10 repeated processes was performed to generate the susceptibility map. In addition, this procedure allowed the reliability of the susceptibility map to be assessed, in terms of variables, importance and role of predisposing factors selected, prediction ability, and accuracy in the assessed probabilities for each pixel of the map. In addition to exploiting the 10-fold cross-validation, the mean value and standard deviation for the probability estimates of each pixel were computed and reported in the susceptibility and uncertainty map. The results showed that the MaxEnt method has high values of accuracy (>0.90), of the kappa coefficient (>0.80), and AUC (>0.92). Furthermore, the achieved findings showed that the capacity of the method used for mapping gully erosion susceptibility is quite robust when the training and testing sets are changed through the 10-fold cross-validation technique. Full article

This study describes a new methodology for estimating gully widths based on their digitized borders. The procedure adapts a previous method developed to determine the mean displacement between two 3D linestrings, considering them continuously, which represents an advance over conventional approaches. In addition to the calculation of the average horizontal distance, it also considers the calculation of widths by sections of a given length in order to analyze differences in their behavior compared to the results for the entire gully. The method is also adapted to multi-temporal studies to analyze the evolution of the gully by comparing width values from several dates. Application was carried out with a large number of linestrings representing gullies of a wide area of olive groves, which were digitized from orthoimages with 0.5 m resolution of two dates. The results demonstrate the feasibility of the proposed method for characterizing gullies and analyzing their evolution between several dates both completely and by sections, allowing the detection of critical areas of gully development. Therefore, these results can be used as input data to improve gully erosion susceptibility maps and to define zones for preventive or corrective actions. Full article

Pluvial floods caused by extreme overland flow inland account for half of all flood damage claims each year along with fluvial floods. In order to increase confidence in pluvial flood susceptibility mapping, overland flow models need to be intensively evaluated using observations from past events. However, most remote-sensing-based flood detection techniques only focus on the identification of degradations and/or water pixels in the close vicinity of overflowing streams after heavy rainfall. Many occurrences of pluvial-flood-induced damages such as soil erosion, gullies, landslides and mudflows located further away from the stream are thus often unrevealed. To fill this gap, a transferable remote sensing fusion method called FuSVIPR, for Fusion of Sentinel-2 & Very high resolution Imagery for Pluvial Runoff, is developed to produce damage-detection maps. Based on very high spatial resolution optical imagery (from Pléiades satellites or airborne sensors) combined with 10 m change images from Sentinel-2 satellites, the Random Forest and U-net machine/deep learning techniques are separately trained and compared to locate pluvial flood footprints on the ground at 0.5 m spatial resolution following heavy weather events. In this work, three flash flood events in the Aude and Alpes-Maritimes departments in the South of France are investigated, covering over more than 160 km² of rural and periurban areas between 2018 and 2020. Pluvial-flood-detection accuracies hover around 75% (with a minimum area detection ratio for annotated ground truths of 25%), and false-positive rates mostly below 2% are achieved on all three distinct events using a cross-site validation framework. FuSVIPR is then further evaluated on the latest devastating flash floods of April 2022 in the Durban area (South Africa), without additional training. Very good agreement with the impact maps produced in the context of the International Charter “Space and Major Disasters” are reached with similar performance figures. These results emphasize the high generalization capability of this method to locate pluvial floods at any time of the year and over diverse regions worldwide using a very high spatial resolution visible product and two Sentinel-2 images. The resulting impact maps have high potential for helping thorough evaluation and improvement of surface water inundation models and boosting extreme precipitation downscaling at a very high spatial resolution. Full article

This study aimed at (i) developing, evaluating and comparing the performance of support vector machines (SVM), boosted regression trees (BRT), random forest (RF) and logistic regression (LR) models in mapping gully erosion susceptibility, and (ii) determining the important gully erosion conditioning factors (GECFs) in a Kenyan semi-arid landscape. A total of 431 geo-referenced gully erosion points were gathered through a field survey and visual interpretation of high-resolution satellite imagery on Google Earth, while 24 raster-based GECFs were retrieved from the existing geodatabases for spatial modeling and prediction. The resultant models exhibited excellent performance, although the machine learners outperformed the benchmark LR technique. Specifically, the RF and BRT models returned the highest area under the receiver operating characteristic curve (AUC = 0.89 each) and overall accuracy (OA = 80.2%; 79.7%, respectively), followed by the SVM and LR models (AUC = 0.86; 0.85 & OA = 79.1%; 79.6%, respectively). In addition, the importance of the GECFs varied among the models. The best-performing RF model ranked the distance to a stream, drainage density and valley depth as the three most important GECFs in the region. The output gully erosion susceptibility maps can support the efficient allocation of resources for sustainable land management in the area. Full article

Gully erosion is the most intensive type of water erosion and it leads to land degradation across the world. Therefore, analyzing the spatial occurrence of this phenomenon is crucial for land management. The objective of this research was to predict gully erosion susceptibility in the Kakia-Esamburmbur catchment in Narok, Kenya, which is badly affected by gully erosion. GIS and ensemble techniques using weight of evidence (WoE) and logistic regression (LR) models were used to map the susceptibility to gully erosion. First, 130 gullies were detected in the study area and portioned out 70:30 for training and validation, respectively. Nine gully erosion conditioning factors were selected as predictors. The relationships between the gully locations and the factors were identified and quantified using WoE, LR and WoE–LR ensemble models. The results show that land use/cover, distance to road, sediment transport index (STI) and topographic wetness index (TWI) are the factors that have the most influence on gully occurrence in the catchment. Additionally, the WoE–LR model performed better than the WoE and LR models, producing an AUC value of 0.88, which was higher than that of the WoE model, 0.62 and the LR model, 0.63. Therefore, the WoE–LR ensemble model is useful in gully erosion susceptibility mapping and is of help to decision makers in land-use planning. Full article