Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm
Pourghasemi, Hamid Reza
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Physical Science and Engineering (PSE) Division
Online Publication Date2018-09-25
Print Publication Date2018-11
Permanent link to this recordhttp://hdl.handle.net/10754/631612
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AbstractThis study aims to assess gully erosion susceptibility and delineate gully erosion-prone areas in Toroud Watershed, Semnan Province, Iran. Two different methods, namely, logistic regression (LR) and evidential belief function (EBF), were evaluated, and a new ensemble method was proposed using the combination of both methods. We initially created a gully erosion inventory map using different resources, including early reports, Google Earth images, and Global Positioning System-aided field surveys. We subsequently split this information randomly and selected 70% (90) of the gullies for calibration and 30% (38) for validation. The method was constructed using a combination of morphometric and thematic predictors that include 16 conditioning parameters. We also assessed the following: (a) potential multicollinearity issues using tolerance and variance inflation factor indices and (b) covariate effects using LR coefficients and EBF class weights. Results show that land use/land cover, lithology, and distance to roads dominate the method with the greatest effect on gully occurrences. We produced three susceptibility maps and evaluated their predictive power through area under the curve (AUC) and seed cell area index analyses. AUC results revealed that the ensemble method presented a considerably higher performance (AUC = 0.909) than did the individual LR (0.802) and EBF (0.821) methods. Similarly, seed cell area index displayed a constant decrease from the ensemble to single methods. The resulted gully erosion-susceptibility map could be used by decision makers and local managers for soil conservation, and for minimising damages in development activities including construction of infrastructures such as roads and the route of gas and electricity transmission lines.
CitationArabameri A, Pradhan B, Rezaei K, Yamani M, Pourghasemi HR, et al. (2018) Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm. Land Degradation & Development 29: 4035–4049. Available: http://dx.doi.org/10.1002/ldr.3151.
SponsorsThanks to four anonymous reviewers, Professor C. J. Barrow for the editorial comments, and Professor Marques and María Jose for their valuable comments, which helped us to improve the quality of the manuscript.
JournalLand Degradation & Development