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

Viet-Ha Nhu,Binh Thai Pham,Fatemeh Mansoorypoor,Saeid Janizadeh,Abolfazl Jaafari,Saro Lee,John J Clague,Ataollah Shirzadi,Ebrahim Omidvar,Mohammadtaghi Avand,Himan Shahabi,Wei Chen,Mohsen Farzin,Baharin Bin Ahmad

doi:10.3390/app10062039

Abstract

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.

Highlights

A global problem that seriously threatens soil and water resources is soil erosion [1,2,3]
We examined the multi-collinearity of gully erosion conditioning factors using the variance inflation factor (VIF) and tolerances (TOL)
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]

Summary

Introduction

A global problem that seriously threatens soil and water resources is soil erosion [1,2,3]. Erosion affects soil productivity, can trigger debris landslides and debris flows [4,5], and—if sufficiently severe—can cause an undesirable buildup of sediment in waterways, reservoirs, and ponds [6,7]. Gullies are deep erosional channels on slopes and are commonly a product of ephemeral runoff during periods of heavy rainfall. They provide pathways for water and sediment transport from the upper to lower parts of watersheds. Networks lower the water table in eroded areas, reducing soil moisture and potentially lowering crop yields on the damaged terrain

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