Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree)

Hosseinalizadeh, Mohsen; Kariminejad, Narges; Chen, Wei; Pourghasemi, Hamid Reza; Alinejad, Mohammad; Behbahani, Ali Mohammadian; Tiefenbacher, John P.

doi:10.1016/j.geomorph.2019.01.006

Cited by 69 publications

(35 citation statements)

References 51 publications

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“…Examination of the relative importance of GECFs shows that slope, TPI, and elevation were the most important in the study area and corroborates [11][12][13]23]. Zabihi et al [13] tested three models (FR, WoE and IoE) to model GE in Iran and found that of 12 GECFs, elevation and LU/LC were the most important in their study area.…”

Section: Discussionmentioning

confidence: 60%

“…ML has proven to be efficient for GE modeling due to its ability to handle small training sets and factors with complex relationships. The most successful ML models for GE consist of multivariate adaptive regression spline [17], maximum entropy (ME) [18], boosted regression tree [19], artificial neural network (ANN) [20], random forest (RF) [21], linear discriminant analysis [22], bagging best-first decision tree [23], support vector machine [24], classification and regression trees [20], and flexible discriminant analysis [14], generalized linear model (GLM) [25], functional data analysis (FDA) [26], and the technique for order preference by similarity to the ideal solution (TOPSIS) [27].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Arabameri

Blaschke

Pradhan

et al. 2020

Sensors

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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

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Section: Discussionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Arabameri

Blaschke

Pradhan

et al. 2020

Sensors

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“…Although it is difficult to directly compare the results of this study with those reported from other regions, we suggest that our ensemble models perform better than the generalized linear model (AUC = 0.71), boosted regression tree (AUC = 0.84), multivariate adaptive regression spline (AUC = 0.83), and ANN (AUC = 0.84) models used by Garosi et al [104]; the certainty factor model (AUC = 0.82) used by Azareh et al [82]; and the Fisher's linear discriminant analysis (AUC = 0.76), logistic model tree (AUC = 0.77), and NBT (AUC = 0.78) models of Arabameri et al [125]. In contrast, however, our models were outperformed by the maximum entropy (AUC = 0.88, 0.90) models used by Azareh et al (2019) and Kariminejad et al [107]; BFTree and its ensembles (bagging and RS) (AUC = 0.92) used by Hosseinalizadeh et al [81]; and the multivariate additive regression splines (AUC = 0.91), SVM (AUC = 0.88), and FR (AUC = 0.96) models employed by Gayen et al [126]. Again, these different results are attributable to local differences in the environments in which the models were used.…”

Section: Discussionmentioning

confidence: 64%

“…These methods extract related patterns in historical data to predict future events [73]. Data mining methods used to predict gully erosion include logistic regression (LR) [2,30,[74][75][76][77], artificial neural network (ANN) [20,48,[78][79][80], random subspace (RS) [48,62,81], maximum entropy (ME) [82], artificial neural fuzzy system (ANFIS) [56,[83][84][85][86], support vector machine (SVM) [18,59,73], fuzzy analytical network (FAN) [37], multi-criteria decision analysis (MCDA) [87,88], evidential belief function (EBF) [88,89], classification and regression tree (CART) [90,91], random forest (RF) [39,52,[92][93][94], rotation forest (RoF) [95], weights of evidence (WofE) [96], frequency ratio (FR) [28,97], BFTree for gully headcut [81], boosted regression [24], ADTree, RF-ADTree [73,76,98], and naive B...…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

et al. 2020

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Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision–recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.

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“…In the last decades, several gully-erosion models have been developed to aid gully-erosion susceptibility mapping (GESM). These models can be classified into three groups: knowledge based models like the analytic hierarchy process (AHP) [16]; bivariate and multivariate statistical models like conditional probability (CP) [28], information value (IV) [29], frequency ratio (FR) [13], index of entropy (IoE) [30], evidential belief function (EBF) [14], weights-of-evidence (WOE) [31], certainty factor (CF) [32], and logistic regression (LR) [33]; and machine-learning models like maximum entropy (ME) [19], multivariate adaptive regression spline (MARS) [15], artificial neural network (ANN) [34], boosted regression tree (BRT) [35], linear discriminant analysis (ADA) [17], bagging best-first decision tree (Bag-BFTree) [36], random forest (RF) [37], flexible discriminant analysis (FDA) [38], support vector machine (SVM) [39], and classification and regression trees (CART) [15].…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence

Arabameri

Cerdà

Tiefenbacher

2019

Water

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Gully erosion is an environmental problem in arid and semi-arid areas. Gullies threaten the soil and water resources and cause off- and on-site problems. In this research, a new hybrid model combines the index-of-entropy (IoE) model with the weight-of-evidence (WoE) model. Remote sensing and GIS techniques are used to map gully-erosion susceptibility in the watershed of the Bastam district of Semnan Province in northern Iran. The performance of the hybrid model is assessed by comparing the results with from models that use only IoE or WoE. Three hundred and three gullies were mapped in the study area and were randomly classified into two groups for training (70% or 212 gullies) and validation (30% or 91 gullies). Eighteen topographical, hydrological, geological, and environmental conditioning factors were considered in the modeling process. Prediction-rate curves (PRCs) and success-rate curves (SRCs) were used for validation. Results from the IoE model indicate that drainage density, slope, and rainfall factors are the most important factors promoting gullying in the study area. Validation results indicate that the ensemble model performed better than either the IoE or WoE models. The hybrid model predicted that 38.02 percent of the study area has either high or very high susceptible to gullying. Given the high accuracy of the novel hybrid model, this scientific methodology may be very useful for land use management decisions and for land use planning in gully-prone regions. Our research contributes to achieve Land Degradation Neutrality as will help to design remediation programs to control non-sustainable soil erosion rates.

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Spatial modelling of gully headcuts using UAV data and four best-first decision classifier ensembles (BFTree, Bag-BFTree, RS-BFTree, and RF-BFTree)

Cited by 69 publications

References 51 publications

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence

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