Identification of soil erosion-susceptible areas using revised universal soil loss equation, analytical hierarchy process and the fuzzy logic approach in sub-watersheds Boussellam and K’sob Algeria

• Published in: Environmental earth sciences 2024, Vol.83 (1), p.34, Article 34
• Main Authors: Benaiche, Morad, Mokhtari, Elhadj, Berghout, Ali, Abdelkebir, Brahim, Engel, Bernard
• Format: Article
• Language: English
• Subjects: Analytic hierarchy process; Biogeosciences; Drainage density; Earth and Environmental Science; Earth Sciences; Environmental Science and Engineering; Fuzzy logic; Geochemistry; Geographic information systems; Geographical information systems; Geology; Hierarchies; Hydrology/Water Resources; Information systems; Land cover; Land use; Lithology; Modelling; Original Article; Rainfall; Remote sensing; Soil analysis; Soil erosion; Soil loss; Terrestrial Pollution; Water; Water erosion; Watersheds
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-023-11339-7

Water erosion in general is considered a natural danger that worsens with passing of years, and this is in the absence of human awareness and taking the necessary preventive measures to manage areas exposed to erosion. Most studies on water erosion have been subject to one framework, which is the evaluation of water erosion. This research contributed to the identification of soil erosion-prone areas in the sub-watershed of Boussellam and K’sob, Algeria, through the use of the Analytical Hierarchy Process (AHP), Fuzzy Boolean Modeling and the Modified Global Soil Loss Equation (RUSLE). A group of factors that affect soil erosion were used (Curvature, Slope, Aspect, Elevation, Rainfall Erosivity, Land use land cover, Drainage Density, K factor/Soil, Lithology, NDWI, Factor Ls, NDVI, Factor P, C Factor). Water erosion conditioning factors were integrated and erosion risk maps were extracted using the Geographic Information System. The study results, after using the RUSLE, fuzzy and AHP models, indicate areas with very high erosion risks occupied 12.91%, 12.98% and 9.49%, respectively, of the study area, and with the help of Receiver Operating Characteristics curves the risk maps were validated. Water erosion under the curve (AUC) and the results revealed that the models showed good predictive capabilities in determining soil erosion susceptibility zones with AUC values of 0.783, 0.806, and 0.644 for the AHP, fuzzy logic and RUSLE models, respectively. This study contributed to identifying the area’s most vulnerable to water erosion to help decision-makers to intervene quickly and take the essential strategies to reduce this problem in the research area.