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dc.contributor.authorGhorbanzadeh, O
dc.contributor.authorShahabi, H
dc.contributor.authorMirchooli, F
dc.contributor.authorValizadeh Kamran, K
dc.contributor.authorLim, S
dc.contributor.authorAryal, J
dc.contributor.authorJarihani, B
dc.contributor.authorBlaschke, T
dc.identifier.citationGhorbanzadeh, O., Shahabi, H., Mirchooli, F., Valizadeh Kamran, K., Lim, S., Aryal, J., Jarihani, B. & Blaschke, T. (2020). Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi-collinearity analysis and K-fold cross-validation. Geomatics, Natural Hazards and Risk, 11 (1), pp.1653-1678.
dc.description.abstractGully erosion is a severe form of soil erosion that results in a wide range of environmental problems such as, dams’ sedimentation, destruction of transportation and energy transmission lines, decreasing and losing farmland productivity, and land degradation. The main objective of this study is to accurately map the areas prone to gully erosion, by developing two machine learning (ML) models, namely artificial neural network (ANN) and random forest (RF) models within 4-fold cross-validation (CV). Moreover, we used the multi-collinearity analysis to select 11 variables among 15 conditioning factors to train the ML models for gully erosion susceptibility mapping (GESM). Lamerd county, Iran, is chosen for a study area because Lamerd county is one of the most affected areas by gully erosion in this country. From 232 gully samples, 75% was used to train the two ML models and the rest of the samples (25%) were used to validate the generated GEMSs using 4-fold CV. The RF model produced a higher accuracy with an accuracy value of 93%. The GEMS generated by the RF model shows that the areas classified as highly vulnerable and very highly vulnerable are 1,869 ha and 5,148 ha, respectively. Results from the two models indicated that the most vulnerable land use/landcover class is bare land because of the low vegetation cover. The outcome of this study can help managers in Lamerd county to mitigate the soil erosion problem and prevent future gully erosion by taking preventive measures.
dc.publisherTaylor & Francis Open
dc.titleGully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi-collinearity analysis and K-fold cross-validation
dc.typeJournal Article
melbourne.affiliation.departmentInfrastructure Engineering
melbourne.source.titleGeomatics, Natural Hazards and Risk
dc.rights.licenseCC BY
melbourne.openaccess.statusPublished version
melbourne.contributor.authorAryal, Jagannath
melbourne.accessrightsAccess this item via the Open Access location

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