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Modelling gully-erosion susceptibility in a semi-arid region, Iran: investigation of applicability of certainty factor and maximum entropy models

Azareh, Ali and Rahmati, Omid and Sardooi, Elham Rafiei and Sankey, Joel B. and Lee, Saro and Shahabi, Himan and Ahmad, Baharin (2019) Modelling gully-erosion susceptibility in a semi-arid region, Iran: investigation of applicability of certainty factor and maximum entropy models. Science of the Total Environment, 655 . pp. 684-696. ISSN 0048-9697

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Official URL: http://dx.doi.org/10.1016/j.scitotenv.2018.11.235

Abstract

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.

Item Type:Article
Uncontrolled Keywords:gully, machine learning, maximum entropy
Subjects:H Social Sciences > H Social Sciences (General)
N Fine Arts > NA Architecture
Divisions:Built Environment
ID Code:87540
Deposited By: Yanti Mohd Shah
Deposited On:08 Nov 2020 04:06
Last Modified:08 Nov 2020 04:06

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