scholarly journals Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1369 ◽  
Author(s):  
Alireza Arabameri ◽  
Biswajeet Pradhan ◽  
Hamid Reza Pourghasemi ◽  
Khalil Rezaei ◽  
Norman Kerle
Keyword(s):  
Data Mining ◽  
Spatial Relationship ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Drainage Density ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Boosted Regression Tree ◽  
Area Index

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

scholarly journals Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

2020 ◽  
Vol 12 (21) ◽  
pp. 3620
Author(s):  
Indrajit Chowdhuri ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Land Use Changes ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Gully Erosion ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Chotanagpur Plateau ◽  
Additive Regression

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

scholarly journals A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)

2021 ◽  
Vol 13 (2) ◽  
pp. 682
Author(s):  
Ali Azedou ◽  
Said Lahssini ◽  
Abdellatif Khattabi ◽  
Modeste Meliho ◽  
Nabil Rifai
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Training Dataset ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Predictive Capacity ◽  
Slope Length ◽  
Rural Municipality

Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods).

scholarly journals Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1313 ◽  
Author(s):  
Sunil Saha ◽  
Jagabandhu Roy ◽  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Dieu Tien Bui
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Gully Erosion ◽  
Machine Learning Techniques ◽  
Weight Of Evidence ◽  
Validation Dataset ◽  
Boosted Regression Tree ◽  
Area Index ◽  
Statistical Measures

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.

scholarly journals Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3451 ◽  
Author(s):  
Usman Salihu Lay ◽  
Biswajeet Pradhan ◽  
Zainuddin Bin Md Yusoff ◽  
Ahmad Fikri Bin Abdallah ◽  
Jagannath Aryal ◽  
...  
Keyword(s):  
Data Mining ◽  
Debris Flow ◽  
Airborne Lidar ◽  
Multivariate Adaptive Regression Splines ◽  
Slope Aspect ◽  
Support Vector ◽  
Mountainous Area ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Debris Flow Susceptibility

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.

scholarly journals Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pawan Gautam ◽  
Tetsuya Kubota ◽  
Aril Aditian
Keyword(s):  
Land Cover ◽  
Landslide Susceptibility ◽  
Total Curvature ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Area Index ◽  
Causal Factors ◽  
Causative Factors

AbstractThe main objective of this study is to understand the overall impact of earthquake in upper Indrawati Watershed, located in the high mountainous region of Nepal. Hence, we have assessed the relationship between the co-seismic landslide and underlying causative factors as well as performed landslide susceptibility mapping (LSM) to identify the landslide susceptible zone in the study area. We assessed the landslides distribution in terms of density, number, and area within 85 classes of 13 causal factors including slope, aspect, elevation, formation, land cover, distance to road and river, soil type, total curvature, seismic intensity, topographic wetness index, distance to fault, and flow accumulation. The earthquake-induced landslide is clustered in Northern region of the study area, which is dominated by steep rocky slope, forested land, and low human density. Among the causal factors, 'slope' showed positive correlation for landslide occurrence. Increase in slope in the study area also escalates the landslide distribution, with highest density at 43%, landslide number at 4.34/km2, and landslide area abundance at 2.97% in a slope class (> 50°). We used logistic regression (LR) for LSM integrating with geographic information system. LR analysis depicts that land cover is the best predictor followed by slope and distance to fault with higher positive coefficient values. LSM was validated by assessing the correctly classified landslides under susceptibility categories using area under curve (AUC) and seed cell area index (SCAI). The LSM approach showed good accuracy with respective AUC values for success rate and prediction rate of 0.843 and 0.832. Similarly, the decreasing SCAI value from very low to very high susceptibility categories advise satisfactory accuracy of the LSM approach.

scholarly journals GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ogbonnaya Igwe ◽  
Ugwuoke Ikechukwu John ◽  
Onwuka Solomon ◽  
Ozioko Obinna
Keyword(s):  
Soil Texture ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Map ◽  
Large Area ◽  
Topographic Wetness Index ◽  
Ahp Model ◽  
Susceptibility Modeling ◽  
Very High

AbstractGully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

scholarly journals Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images

2019 ◽  
Vol 11 (19) ◽  
pp. 2285 ◽  
Author(s):  
Jeong-Cheol Kim ◽  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Remote Sensing ◽  
Specific Capacity ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Groundwater Potential ◽  
The Other ◽  
Thematic Maps ◽  
Remote Sensing Images ◽  
Boosted Regression Tree ◽  
Productivity Data

This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. Two of these three models are data mining models: Boosted Regression Tree (BRT) model and Random Forest (RF) model. The other model is the Logistic Regression (LR) model. The three models are based on the relationship between groundwater-productivity data (specific capacity (SPC) and transmissivity (T)) and the related hydro-geological factors from thematic maps, such as topography, lineament, geology, land cover, and etc. The thematic maps which are generated from the remote sensing images. Groundwater productivity data were collected from 86 wells locations. The resulting GPP maps were validated through area-under-the-curve (AUC) analysis using wells data that had not been used for training the model. When T was used in the BRT, RF, and LR models, the obtained GPP maps had 81.66%, 80.21%, and 85.04% accuracy, respectively, and when SPC was used, the maps had 81.53%, 78.57%, and 82.22% accuracy, respectively. The LR model, which is a statistical model, showed the highest verification accuracy, also the other two models showed high accuracies. These observations indicate that all three models can be useful for groundwater resource development.

scholarly journals GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

2020 ◽  
Author(s):  
Ogbonnaya Igwe ◽  
Ikechukwu John Ugwuoke ◽  
Onwuka Solomon ◽  
Ozioko Obinna
Keyword(s):  
Soil Texture ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Map ◽  
Large Area ◽  
Topographic Wetness Index ◽  
Ahp Model ◽  
Susceptibility Modeling ◽  
Very High

Abstract Gully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

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