A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)

Ali Azedou, Said Lahssini, Abdellatif Khattabi, Modeste Meliho and Nabil Rifai
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Ali Azedou: National School of Forestry Engineering, Salé 11000, Morocco
Said Lahssini: National School of Forestry Engineering, Salé 11000, Morocco
Abdellatif Khattabi: National School of Forestry Engineering, Salé 11000, Morocco
Modeste Meliho: Moroccan Association of Regional Sciences (AMSR), Rabat 10080, Morocco
Nabil Rifai: Forest Department Ministry of Agriculture, Fisheries, Rural Development and Water and Forests, Rabat 10080, Morocco

Sustainability, 2021, vol. 13, issue 2, 1-30

Abstract: Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods).

Keywords: gully erosion; frequency ratio; logistic regression; random forest; rural municipality of El Faid; Morocco (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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