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• W4309593039 endingPage "429" @default.
• W4309593039 startingPage "429" @default.
• W4309593039 abstract "Gully erosion susceptibility mapping (GESM) through predicting the spatial distribution of areas prone to gully erosion is required to plan gully erosion control strategies relevant to soil conservation. Recently, machine learning (ML) models have received increasing attention for GESM due to their vast capabilities. In this context, this paper sought to review the modeling procedure of GESM using ML models, including the required datasets and model development and validation. The results showed that elevation, slope, plan curvature, rainfall and land use/cover were the most important factors for GESM. It is also concluded that although ML models predict the locations of zones prone to gullying reasonably well, performance ranking of such methods is difficult because they yield different results based on the quality of the training dataset, the structure of the models, and the performance indicators. Among the ML techniques, random forest (RF) and support vector machine (SVM) are the most widely used models for GESM, which show promising results. Overall, to improve the prediction performance of ML models, the use of data-mining techniques to improve the quality of the dataset and of an ensemble estimation approach is recommended. Furthermore, evaluation of ML models for the prediction of other types of gully erosion, such as rill–interill and ephemeral gully should be the subject of more studies in the future. The employment of a combination of topographic indices and ML models is recommended for the accurate extraction of gully trajectories that are the main input of some process-based models." @default.
• W4309593039 created "2022-11-28" @default.
• W4309593039 creator A5005916070 @default.
• W4309593039 creator A5029343917 @default.
• W4309593039 creator A5030940853 @default.
• W4309593039 creator A5077536887 @default.
• W4309593039 date "2022-11-22" @default.
• W4309593039 modified "2023-09-25" @default.
• W4309593039 title "Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review" @default.
• W4309593039 cites W1941919908 @default.
• W4309593039 cites W1964396812 @default.
• W4309593039 cites W1969756181 @default.
• W4309593039 cites W1970483883 @default.
• W4309593039 cites W1975246819 @default.
• W4309593039 cites W1976405057 @default.
• W4309593039 cites W1977069065 @default.
• W4309593039 cites W1978110877 @default.
• W4309593039 cites W1980276147 @default.
• W4309593039 cites W1981039744 @default.
• W4309593039 cites W1981372915 @default.
• W4309593039 cites W1981646498 @default.
• W4309593039 cites W1982623886 @default.
• W4309593039 cites W1988650824 @default.
• W4309593039 cites W1990105406 @default.
• W4309593039 cites W1994214164 @default.
• W4309593039 cites W1996381847 @default.
• W4309593039 cites W1998432757 @default.
• W4309593039 cites W2002271711 @default.
• W4309593039 cites W2002620848 @default.
• W4309593039 cites W2003192844 @default.
• W4309593039 cites W2003879311 @default.
• W4309593039 cites W2009296710 @default.
• W4309593039 cites W2012118327 @default.
• W4309593039 cites W2014727276 @default.
• W4309593039 cites W2015012163 @default.
• W4309593039 cites W2021302788 @default.
• W4309593039 cites W2030949476 @default.
• W4309593039 cites W2032477387 @default.
• W4309593039 cites W2035254679 @default.
• W4309593039 cites W2035549409 @default.
• W4309593039 cites W2039014549 @default.
• W4309593039 cites W2039615753 @default.
• W4309593039 cites W2042229599 @default.
• W4309593039 cites W2049466036 @default.
• W4309593039 cites W2051718657 @default.
• W4309593039 cites W2053669534 @default.
• W4309593039 cites W2053787890 @default.
• W4309593039 cites W2054130874 @default.
• W4309593039 cites W2060775322 @default.
• W4309593039 cites W2063907334 @default.
• W4309593039 cites W2064056597 @default.
• W4309593039 cites W2069663627 @default.
• W4309593039 cites W2073350698 @default.
• W4309593039 cites W2075125986 @default.
• W4309593039 cites W2077850282 @default.
• W4309593039 cites W2079066428 @default.
• W4309593039 cites W2079109804 @default.
• W4309593039 cites W2086610197 @default.
• W4309593039 cites W2087741510 @default.
• W4309593039 cites W2088730795 @default.
• W4309593039 cites W2101926042 @default.
• W4309593039 cites W2106056097 @default.
• W4309593039 cites W2110985329 @default.
• W4309593039 cites W2111825008 @default.
• W4309593039 cites W2127573370 @default.
• W4309593039 cites W2131184341 @default.
• W4309593039 cites W2146852822 @default.
• W4309593039 cites W2149064628 @default.
• W4309593039 cites W2153944160 @default.
• W4309593039 cites W2155653793 @default.
• W4309593039 cites W2164777277 @default.
• W4309593039 cites W2171612326 @default.
• W4309593039 cites W2192410920 @default.
• W4309593039 cites W2196046484 @default.
• W4309593039 cites W2215874512 @default.
• W4309593039 cites W2231576311 @default.
• W4309593039 cites W2251166296 @default.
• W4309593039 cites W2273862374 @default.
• W4309593039 cites W2277297676 @default.
• W4309593039 cites W2278830514 @default.
• W4309593039 cites W2284484756 @default.
• W4309593039 cites W2311094426 @default.
• W4309593039 cites W2474676589 @default.
• W4309593039 cites W2497210317 @default.
• W4309593039 cites W2548849268 @default.
• W4309593039 cites W2551325035 @default.
• W4309593039 cites W2565354029 @default.
• W4309593039 cites W2567326027 @default.
• W4309593039 cites W2569349941 @default.
• W4309593039 cites W2580808806 @default.
• W4309593039 cites W2583778387 @default.
• W4309593039 cites W2601183962 @default.
• W4309593039 cites W2612222728 @default.
• W4309593039 cites W2617146439 @default.
• W4309593039 cites W2738109469 @default.
• W4309593039 cites W2740117919 @default.
• W4309593039 cites W2741517055 @default.
• W4309593039 cites W2757787785 @default.

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