Gully Erosion Susceptibility Mapping Research Articles

Assessing gully erosion susceptibility dynamics using information value and hazard index methods: A case study of Agulu-Nanka watershed, Southeast Nigeria

Gully erosion is one of the major causes of land degradation leading to the loss of productive lands globally. The Benue Trough in Nigeria is plagued with the problem of gully erosion, with a myriad of natural and anthropogenic factors considered as contributory factors. In this study, quantitative and qualitative methods were combined to evaluate gully susceptibility in the Agulu-Nanka watershed located in the lower part of Benue Trough between 2000 and 2018. Landsat 7 and 8 images, Digital Elevation Model (DEM), geological, soil, and rainfall erosivity maps were used to generate the gully erosion controlling factors including vegetation cover, lithology, soil, land use/cover, slope, curvature, aspect and rainfall erosivity. Additionally, high resolution image was used to develop gully inventory map. The information value method was used evaluate the influence of various classes of gully erosion controlling factors on gully distribution in the watershed. This enabled objective derivation of weights for the various classes of these factors. Information value analysis reveal that low percentage vegetation cover (<10 %), Ameki Group lithological unit, high slope gradient (17 − 43°) and Dystric Fluvisols soil type have the highest influence on gully erosion in the watershed. Since the information value method does not consider interrelationship between factors, the rank sum technique was used to derive factor weights based on the relationship between the factors. To develop the gully susceptibility map for the Agulu-Nanka watershed for the years 2000 and 2018, the erosion controlling factors were integrated using factor class weights derived through information value and factor weights derived through rank sum method. The analysis of gully erosion hazard maps show that approximately 48 % of the watershed was under low erosion risk over the period of 18 years. Areas under moderate, high and severe erosion risk decreased from 27.45 %, 9.45 % and 0.27 % of the watershed in year 2000, to 21.30 %, 8.41 %, and 0.20 % of the watershed respectively in 2018. On the other hand, the areas under negligible erosion risk increased from 14.69 % in year 2000, to 21.26 % of the watershed in 2018. The validation of the gully erosion susceptibility map for years 2000 and 2018 using the Receiver Operating Curve (ROC) showed ROC-AUC values of 0.81 % and 0.88 % respectively suggesting good model results. While the result of study indicates that restoration efforts through afforestation is yielding some result, the minimal reduction in the size of areas under severe and high gully erosion susceptibilities indicate that additional mitigation measures need to be combined with intensified afforestation efforts to effectively combat gully erosion in the watershed.

Assessment of gully erosion susceptibility using four data-driven models AHP, FR, RF and XGBoosting machine learning algorithms

Gully erosion is a significant global threat to socioeconomic and environmental sustainability, making it a widespread natural hazard. Developing spatial models for gully erosion is crucial for local governance to effectively implement mitigation measures and promote regional development. This study applied two machine learning (ML) models, RF and XGB, alongside an AHP-based multi-criteria decision method and FR bivariate statistics, to assess gully erosion susceptibility (GES) in the Kangsabati River basin in eastern India's Chotonagpur plateau fringe. A GIS database was created, incorporating recorded gully erosion incidents and 20 conditioning variables, which were evaluated for multicollinearity. These variables served as predictive factors for assessing gully erosion presence in the study area. The models' performance was evaluated using metrics such as RMSE, MAE, specificity, sensitivity, and accuracy. The XGB model outperformed the others, achieving a predictive accuracy of 90.22%. The study found that approximately 6.56% of the Kangsabati catchment is highly susceptible to gully erosion, with 12.39% moderately susceptible and 81.05% not susceptible. The XGB model had the highest ROC value of 85.5 during testing, indicating its superiority over the FR (ROC ​= ​81.7), AHP (ROC ​= ​79.8), and RF (ROC ​= ​83.8) models. These findings highlight the XGB model's efficacy and potential for large-scale GES mapping.

Gully erosion susceptibility mapping and prioritization of gully-dominant sub-watersheds using machine learning algorithms: Evidence from the Silabati River (tropical river, India)

Gully erosion is a severe environmental issue that poses threats to agriculture, human safety, habitats, infrastructure, and soil integrity. Selecting the right machine learning model is vital for accurate Gully Erosion Susceptibility Mapping (GESM) due to varying environmental hazard performance. This study employed a comparative analysis of two machine learning techniques, Random Forest (RF) and Extreme Gradient Boost (XGBoost), to develop a highly precise GESM for the Silabati watershed (India). The analysis incorporated 24 controlling factors and examined a dataset of 460 sample points, with equal representation of gullies and non-gullies. Variance Inflation Factors (VIF) and Information Gain Ratio (IGR) techniques were applied to assess multicollinearity test among the controlling factors. Lithology, elevation, distance from the road, LULC, geomorphology, rainfall, drainage density, and coarse fragments emerged as crucial factors in determining GESM. Statistical tests, including the Kappa index, Root Mean Square Error (RMSE), Accuracy (ACC), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Receiver Operating Characteristic (ROC), were employed to evaluate the RF and XGB models on training and testing data. Both models demonstrated strong performance, with the XGBoost and RF models achieving ROC values of 84.1% and 83.2%, respectively. The applied quantile classification method resulted in the creation of five distinct GESMs, categorized as very high (VH), high (H), moderate (M), low (L), and very low (VL). We found that the very high GESM areas of the watershed were 4.10% in the XGBoost model and 4.61%. in the RF model. Out of the 26 sub-watersheds, the results have identified five sub-watersheds as highly prioritized for sustainable management. Therefore, the present study provides an accurate gully erosion identification using advanced models, offering valuable insights for policymakers to proper management implementation.

Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model

Gully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service and human well-being. Thus, gully erosion susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Here, we proposed four new hybrid Machine learning models, namely weight of evidence -Multilayer Perceptron (MLP- WoE), weight of evidence –K Nearest neighbours (KNN- WoE), weight of evidence - Logistic regression (LR- WoE), and weight of evidence - Random Forest (RF- WoE), for mapping gully erosion exploring the opportunities of GIS tools and Remote sensing techniques in the El Ouaar watershed located in the Souss plain in Morocco. Inputs of the developed models are composed of the dependent (i.e., gully erosion points) and a set of independent variables. In this study, a total of 314 gully erosion points were randomly split into 70% for the training stage (220 gullies) and 30% for the validation stage (94 gullies) sets were identified in the study area. 12 conditioning variables including elevation, slope, plane curvature, rainfall, distance to road, distance to stream, distance to fault, TWI, lithology, NDVI, and LU/LC were used based on their importance for gully erosion susceptibility mapping. We evaluate the performance of the above models based on the following statistical metrics: Accuracy, precision, and Area under curve (AUC) values of receiver operating characteristics (ROC). The results indicate the RF- WoE model showed good accuracy with (AUC = 0.8), followed by KNN-WoE (AUC = 0.796), then MLP-WoE (AUC = 0.729) and LR-WoE (AUC = 0.655), respectively. Gully erosion susceptibility maps provide information and valuable tool for decision-makers and planners to identify areas where urgent and appropriate interventions should be applied.

Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya

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 &amp; 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.

An Ensemble of Weight of Evidence and Logistic Regression for Gully Erosion Susceptibility Mapping in the Kakia-Esamburmbur Catchment, Kenya

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.

Optimizing machine learning algorithms for spatial prediction of gully erosion susceptibility with four training scenarios.

Gully erosion causes high soil erosion rates and is an environmental concern posing major risk to the sustainability of cultivated areas of the world. Gullies modify the land, shape new landforms, and damage agricultural fields. Gully erosion mapping is essential to understand the mechanism, development, and evolution of gullies. In this work, a new modeling approach was employed for gully erosion susceptibility mapping (GESM) in the Golestan Dam basin of Iran. The measurements of 14 gully erosion (GE) factors at 1042 GE locations were compiled in a spatial database. Four training datasets comprised of 100%, 75%, 50%, and 25% of the entire database were used for modeling and validation (for each data set in the common 70:30 ratio). Four machine learning models-maximum entropy (MaxEnt), general linear model (GLM), support vector machine (SVM), and artificial neural network (ANN)- were employed to check the usefulness of the four training scenarios. The results of random forest (RF) analysis indicated that the most important GE effective factors were distance from the stream, elevation, distance from the road, and vertical distance of the channel network (VDCN). The receiver operating characteristic (ROC) was used to validate the results. Our study showed that the sample size influenced the performance of the four machine learning algorithms. However, the ANN had a lower sensitivity to the reduction of sample size. In addition, validation results revealed that ANN (AUROC = 0.85.7-0.90.4%) had the best performance based on all four sample data sets. The results of this research can be useful and valuable guidelines for choosing machine learning methods when a complete gully inventory is not available in a region.

Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review

Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models.

Gully erosion susceptibility considering spatiotemporal environmental variables: Midwest U.S. region

Study regionThe study was tested in Jefferson County in Illinois, USA, whose land use is a typical representation of row crop cultivation in the Midwestern USA. Study focusThis study aimed to predict the gully erosion susceptibility in agricultural land using remote sensed environmental data (topographic, pedologic, land cover, precipitation, and vegetation development) considering their spatio-temporal variability in a modeling framework based on the maximum entropy model MaxEnt. The methodology thoroughly evaluated each environmental factor contributing to gully erosion prediction and used a set of rules based on accuracy, transferability, and efficiency to evaluate the model performance. New Hydrological Insights for the RegionThis study developed a data-driven modeling framework that can be applied across other regions. The modeling framework indicates that fifteen factors were the most relevant for developing the gully erosion susceptibility map, where 7.4% of the agricultural land in the study area was found at elevated risk of developing gully erosion. Slope, land cover, organic matter, seasonal LAI, and maximum daily precipitation were the most contributing environmental factors to the study area. Furthermore, this study identified the importance of high temporal resolution in varying seasonal factors (i.e., leaf area index and precipitation) to improve model predictability compared to annual temporal discretization.

Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach

Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random sub-space (RSS) and rotation forest (RTF) ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility (GES) in Hinglo river basin. 120 gullies were marked and grouped into four-fold. A total of 23 factors including topographical, hydrological, lithological, and soil physio-chemical properties were effectively used. GES maps were built by RBFnn, RSS-RBFnn, and RTF-RBFnn models. The very high susceptibility zone of RBFnn, RTF-RBFnn and RSS-RBFnn models covered 6.75%, 6.72% and 6.57% in Fold-1, 6.21%, 6.10% and 6.09% in Fold-2, 6.26%, 6.13% and 6.05% in Fold-3 and 7%, 6.975% and 6.42% in Fold-4 of the basin. Receiver operating characteristics (ROC) curve and statistical techniques such as mean-absolute-error (MAE), root-mean-absolute-error (RMSE) and relative gully density area (R-index) methods were used for evaluating the GES maps. The results of the ROC, MAE, RMSE and R-index methods showed that the models of susceptibility to gully erosion have excellent predictive efficiency. The simulation results based on machine learning are satisfactory and outstanding and could be used to forecast the areas vulnerable to gully erosion.

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area.

Assessment of the gully erosion susceptibility using three hybrid models in one small watershed on the Loess Plateau

Gully erosion is globally considered a severe environmental problem that causes great damage to arable lands, roads, and forests; therefore, it is crucial to evaluate the gully erosion susceptibility. The objective of this study was to establish three hybrid models based on VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), frequency ratio (FR), random forest (RF), gradient boosting decision tree (GBDT), and randomized tree (ET) data to assess the susceptibility of gully erosion in a small watershed of the Loess Plateau of China. We extracted 540 gullies with 24, 919 gully pixels and 11 conditioning factors were extracted and utilized to establish a gully inventory database. Subsequently, FR was applied to determine the relationships between the conditioning factors and gully pixels, and machine learning methods were used to quantify the relative importance of these conditioning factors. Three hybrid gully erosion susceptibility models, that is, VIKOR-FR-RF, VIKOR-FR-GBDT, and VIKOR-FR-ET, were established for gully erosion susceptibility mapping (GESM). The receiver operating characteristic curve (ROC) and area under the curve (AUC) were utilized to evaluate the performance of these models. The results showed that elevation, distance to road, and the normalized difference vegetation index significantly contributed to the gully occurrences. VIKOR-FR-ET had the highest performance, with an AUC of 0.83, followed by VIKOR-FR-RF (AUC = 0.81) and VIKOR-FR-GBDT (AUC = 0.70). Therefore, we concluded that VIKOR-FR-ET was the most efficient approach for predicting gully erosion susceptibility in a semi-arid region, such as the Loess Plateau. Our results will help design soil and water conservation measures for gully erosion control at small watershed scales.

Designing gully erosion susceptibility maps (GESM) in the Algerian Eastern Tell: a case study of the K’sob River watershed

Designing gully erosion susceptibility maps (GESM) in the Algerian Eastern Tell: a case study of the K’sob River watershed

Choosing the Right Horizontal Resolution for Gully Erosion Susceptibility Mapping Using Machine Learning Algorithms: A Case in Highly Complex Terrain

Gully erosion susceptibility (GES) maps are essential for managing land resources and erosion control. Choosing the optimal horizontal resolution in GES mapping is a challenge. In this study, the optimal resolution for GES mapping in a complex loess hilly area on the Chinese Loess Plateau was tested using two machine learning algorithms. Unmanned aerial vehicle (UAV) images with a 9 cm resolution and GNSS RTK field-measured data were employed as base datasets, and 11 factors were used in the machine learning models. A series of horizontal resolutions, from 0.5–30 m, was used to determine which was the optimal level and how the resolution influenced the GES mapping. The results showed that the optimal resolution for GES mapping was 2.5–5 m in the loess hilly area, for both the random forest (RF) and extreme gradient-boosting (XGBoost) machine learning algorithms employed in this study. High resolutions overestimated the probability of gully erosion in stable regions, and it became difficult to identify gully and non-gully regions at too-coarse resolutions. The variable importance for GES mapping changed with the resolution and varied among variables. Slope gradient, land use, and contributing area were, in general, the three most critical factors. Land use remained an important factor at all the tested resolution levels. The importance of the slope gradient was underestimated at coarse resolutions (10–30 m), and the importance of the contributing area was underestimated at resolutions that were comparatively fine (0.5–1 m). This study provides an essential reference for selecting the optimal resolution for gully mapping, and thus, offers support for approaches attempting to map gullies using UAV.

Bivariate Based Susceptibility Mapping for Gully Erosion in Wanjoga River Catchment Upper Tana Basin, Kenya

Gullies occur in semi-arid regions characterized by rainfall variability and seasonality, increased overland flow, affecting ecological fragility of an area. In most gully prone areas, extent of land affected by gullies is increasing. Thus, predicting susceptibility to gully erosion in semi-arid environment is an important step towards effectively rehabilitating and prevention against gully erosion. Proneness to gully occurrence was assessed against; Land cover/land use, slope, soil characteristics, rainfall variability and elevation, and modelled using geographical information system (GIS)-based bivariate statistical approach. Objectives of the study were; a) to assess influence of geomorphological factors on gully erosion, b) analyze and develop gully erosion susceptibility map, c) verify gully susceptibility images using error matrix of class labels in classified map against ground truth reference data. Total of 66 gullied areas (width and depth ≥ ranging 0.5), were mapped using 15m resolution Landsat images for 2018 and field surveys to estimate susceptibility to gully erosion by Global Mapper software in GIS. The images were verified using 120 pixels of known 15 gully presence or absence to produce an error matrix based on comparison of actual outcomes to predicted outcomes. Influence of conditioning factors to gully erosion showed a significant positive relationship between gully susceptibility and gully conditioning factors with consistency value; CR =0.097; value&lt; 0.1, indicating, individual conditioning factors had an importance in influencing gully erosion. Slope (43%) and soil lithotype (25%), most influenced gully susceptibility, while land cover/land use (12%) and rainfall (12%) had least impact. Verification results showed satisfactory agreement between susceptibility map and field data on gullied areas at approximately 76.2%, an error of positive value of 4% and a negative value of 7%. Thus, production of susceptibility map by bivariate statistical method represents a useful tool for ending long and short-term gully emergencies by planning conservation of semi-arid regions.

Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic features of an area significantly influences on the erosive power of the water flow, it is an important task the extraction of terrain features from DEM to properly research gully erosion. Alongside, topography is highly correlated with other geo-environmental factors i.e. geology, climate, soil types, vegetation density and floristic composition, runoff generation, which ultimately influences on gully occurrences. Therefore, terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility (GES) mapping. In this study, remote sensing-Geographic information system (GIS) techniques coupled with machine learning (ML) methods has been used for GES mapping in the parts of Semnan province, Iran. Current research focuses on the comparison of predicted GES result by using three types of DEM i.e. Advanced Land Observation satellite (ALOS), ALOS World 3D-30 m (AW3D30) and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) in different resolutions. For further progress of our research work, here we have used thirteen suitable geo-environmental gully erosion conditioning factors (GECFs) based on the multi-collinearity analysis. ML methods of conditional inference forests (Cforest), Cubist model and Elastic net model have been chosen for modelling GES accordingly. Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods (Cforest = 21.4, Cubist = 19.65 and Elastic net = 17.08), followed by lithology and slope. Validation of the model’s result was performed through area under curve (AUC) and other statistical indices. The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs (AUC value of Cforest in ALOS DEM is 0.994, AW3D30 DEM is 0.989 and ASTER DEM is 0.982) used in this study, followed by elastic net and cubist model. The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

Modeling gully erosion susceptibility in Phuentsholing, Bhutan using deep learning and basic machine learning algorithms

The present study attempts to demarcate the areas susceptible to gully erosion in Phuentsholing, Bhutan, using Deep Learning CNN (convolution neural network) and artificial neuron network (ANN), Support Vector Machine (SVM) and maximum entropy, three basic machine learning techniques in the GIS setting. Application of deep learning technique is new in the field of gully erosion. Considering the 240 gully pixels and seventeen gully erosion conditioning factors (GECFs), the gully erosion susceptibility maps (GESMs) were prepared. Out of the 240 gully pixels, 70% were used as training datasets and 30% were used as validation datasets for modeling and judging the GESMs. The GECFs were selected based on the previous literatures and multi-collinearity test. The importance of the GECFs was assessed by the chi-square attribute evaluation (CSEA) and random forest (RF) methods. Finally, applying the receiver operating characteristics’ area under curve (AUC-ROC), RMSE, MAE and R-index, the robustness of the GESMs was evaluated and compared. The GESMs were classified using natural break classification method into very high, high, moderate, low and very low susceptible classes. Nearly, 20% of the study area has very high susceptibility to gully erosion. As per the results of CSEA and RF methods, sand concentration, land use\cover and altitudes have the largest contribution in making the area very susceptible to gully erosion. Results of the validation techniques recognized the entire selected model as accurate and robust. Among the selected models, the capability of CNN model (AUC = 0.910, MAE = 0.029, RMSE = 0.171 for training data and AUC = 0.929, MAE = 0.089, RMSE = 0.299 for testing data) in predicting the gully erosion susceptibility is higher than other models. The produced GESMs will be helpful to the researchers as well as decision makers in establishing gully erosion management strategies.

Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements

Water dominated gullies formation and associated land degradation are the foremost challenges among the planners for sustainability and optimization of land resources. This type of hazardous phenomenon is utmost vulnerable due to huge loss of surface soil in the sub-tropical developing countries like India. The present study has been carried out in rugged badland topography of Garhbeta-I Community Development (C.D.) Block in eastern India for assessing the gully erosion susceptibility (GES) mapping and optimization of land use planning. The GES mapping is the first and foremost steps towards minimization this adverse affect and attaining sustainable development. In this study we also describe the importance of plantation and alternation of ex-situ tree species with in-situ species for minimizes the erosional activity. To meet our research goal here we used two prediction based machine learning algorithm (MLA) namely random forest (RF) and boosted regression tree (BRT) and one optimization model of Ecogeography based optimization (EBO). The research study also carried out by using a total of 199, in which 139 (70%) and 60 (30%) gully head-cut points were used for training and validation purposes respectively and treated as dependent factors, and twenty gully erosion conditioning factors as independent variables. These models are validated through receiver operating characteristics-area under the curve (ROC-AUC), accuracy (ACC), precision (PRE) and Kappa coefficient index analysis. The validation result showed that EBO model with the highest values of AUC-0.954, ACC-0.85, PRE-0.877 and Kappa-0.646 is the most accurate model for GES followed by BRT and RF. The outcome results should help for the sustainable development of this rugged badland topography.