Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion

Garosi, Younes; Sheklabadi, Mohsen; Conoscenti, Christian; Pourghasemi, Hamid Reza; Oost, Kristof Van

doi:10.1016/j.scitotenv.2019.02.093

Cited by 160 publications

(73 citation statements)

References 97 publications

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“…An examination of the literature suggests that conditioning factors for gully erosion are area-specific and cannot be reliably extrapolated to other regions. For example, Amiri et al [121] identified land-use as the most important factor in their study areas, whereas Rahmati et al [122] and Garosi et al [92] reported that distance from rivers is the most important factor in their studies. Furthermore, the slope factor, which we and Rahmati et al [122] ranked as a relatively unimportant factor, was among the most effective factors identified by Rahmati et al [97].…”

Section: Discussionmentioning

confidence: 98%

“…Most authors who have studied gully erosion consider the heads of gullies to be gully locations [76,99,100], because gully heads are the sources of much of the sediment carried by the gully channels and delivered to the fluvial system below [101,102]. However, some researchers have used grid cells to create gully polygons to prepare gully erosion susceptibility maps [92,103,104], whereas others have converted gully polygons to points using 'feature to point' tool in ArcGIS software [105]. However, an active gully is a dynamic landform, and its head moves landward over time as erosion proceeds.…”

Section: Gully Inventory Mapmentioning

confidence: 99%

“…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%

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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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Section: Discussionmentioning

confidence: 98%

Section: Gully Inventory Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…[19][20][21] While Garosi et al and Goetz et al concluded that random forest method is a better one. 22,23 Using the GAM, time to event, age and grade had signi cant nonlinear effect on the survival of patients with BC so that patients with lower time to event, higher age and higher grade had more mortality. Similarly, previous studies have revealed that the survival of patients with BC is directly related by time and inverse related by age and grade.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Factors Associated with Mortality in Patients with Breast Cancer: A Machine Learning Approach

Jafarabadi

Iraji

Dolatkhah

et al. 2020

Preprint

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Background: Breast cancer (BC) was the fifth leading cause of death worldwide in 2015 and the second leading cause of death in Iran in 2012. This study aimed to model the factors associated with mortality in patients with BC utilizing the machine learning approach.Methods: We used data of patients with primary BC during 2007-2016 in Tabriz, Iran. The data were analyzed using decision tree (DT), boosted tree (BT), random forest (RF), k-nearest neighbors (KNN) and generalized additive model (GAM) with inverse probability of censoring weighting (IPCW) technique to assess the risk factors of mortality. The models were compared by using diagnostic accuracy measures.Results: Accuracy of the models ranged from 76.0 to 93.0%, with sensitivity of 82.5-98.8% and specificity of 72.2-99.4%. The GAM fit the data best with accuracy of 93.0% (95% CI: [90.5, 95.0]), sensitivity of 98.8% (95% CI: [96.9, 99.7]) and specificity of 84.3% (95% CI: [78.8, 88.9]) where non-linear effect of age (p-value = 0.006), grade (p-value = 0.024) and time to event (p-value < 0.001) on mortality were significant. Conclusion: The GAM seems to be an optimal model for classifying the mortality in patients with BC. Considering the time to event, age and grade, as the prognostic factors obtained by GAM, more accurate prevention planning may be designed.

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“…In addition, the kappa coefficient, calculated based on the difference between the observed and predicted landslides, evaluates the reliability of each model. It is calculated as follows [51]:…”

Section: Model Performance Assessmentmentioning

confidence: 99%

Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling

2019

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This study analyzed and compared landslide susceptibility models using decision tree (DT), random forest (RF), and rotation forest (RoF) algorithms at Woomyeon Mountain, South Korea. Out of a total of 145 landslide locations, 102 locations (70%) were used for model training, and the remaining 43 locations (30%) were used for validation. Fourteen landslide conditioning factors were identified, and the contributions of each factor were evaluated using the RRelief-F algorithm with a 10-fold cross-validation approach. Three factors, timber diameter, age, and density had no contribution to landslide occurrence. Landslide susceptibility maps (LSMs) were produced using DT, RF, and RoF models with the 11 remaining landslide conditioning factors: altitude, slope, aspect, profile curvature, plan curvature, topographic position index, elevation-relief ratio, slope length and slope steepness, topographic wetness index, stream power index, and timber type. The performances of the LSMs were assessed and compared based on sensitivity, specificity, precision, accuracy, kappa index, and receiver operating characteristic curves. The results showed that the ensemble learning methods outperformed the single classifier (DT) and that the RoF model had the highest prediction capability compared to the DT and RF models. The results of this study may be helpful in managing areas vulnerable to landslides and establishing mitigation strategies.

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Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion

Cited by 160 publications

References 97 publications

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

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

Modeling the Factors Associated with Mortality in Patients with Breast Cancer: A Machine Learning Approach

Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling

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