Assessing the Global Sensitivity of RUSLE Factors: A Case Study of Southern Bahia, Brazil DOI Creative Commons
Mathurin François, Camila A. Gordon, Ulisses Costa de Oliveira

et al.

Soil Systems, Journal Year: 2024, Volume and Issue: 8(4), P. 125 - 125

Published: Dec. 2, 2024

Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but crucial to rank importance each factor terms non-linear impact on erosion rate. Hence, goal this study was perform a GSA RUSLE for assessment southern Bahia, Brazil. To meet goal, three topographic (LS factor) equations alternately implemented RUSLE, coupled with geographic information system (GIS) software and variogram response surfaces (VARSs), were used. The results showed that average rate Pardo River basin 25.02 t/ha/yr. In addition, slope angle which associated LS most sensitive parameter, followed by cover management (C support practices (P (CP factors), specific catchment area (SCA), sheet (m), erodibility (K factor), rill (n), erosivity (R factor). novelty work values parameters m n can substantially affect and, thus, estimation.

Language: Английский

Transformation of Geospatial Modelling of Soil Erosion Susceptibility Using Machine Learning DOI
Muhammad Ramdhan Olii,

Sartan Nento,

Nurhayati Doda

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 18, 2024

Abstract Soil erosion creates substantial environmental and economic challenges, especially in areas vulnerable to land degradation. This study investigates the use of machine learning (ML) techniques—namely Support Vector Machines (SVM) Generalized Linear Models (GLM)—for geospatial modeling soil susceptibility (SES). By leveraging data incorporating a range factors including hydrological, topographical, variables, research aims improve accuracy reliability SES predictions. Results show that SVM model predominantly identifies as having moderate (40.59%) or low (38.50%) susceptibility, whereas GLM allocates higher proportion very (24.55%) (38.59%) susceptibility. Both models exhibit high performance, with achieving accuracies 87.4% 87.2%, respectively, though slightly surpasses AUC (0.939 vs. 0.916). places greater emphasis on hydrological such distance rivers drainage density, while provides more balanced assessment across various variables. demonstrates ML-based can significantly enhance assessments, offering nuanced accurate approach than traditional methods. The findings highlight value adopting innovative, data-driven techniques offer practical insights for management conservation practices.

Language: Английский

Citations

0
Assessing the Global Sensitivity of RUSLE Factors: A Case Study of Southern Bahia, Brazil DOI Creative Commons
Mathurin François, Camila A. Gordon, Ulisses Costa de Oliveira

et al.

Soil Systems, Journal Year: 2024, Volume and Issue: 8(4), P. 125 - 125

Published: Dec. 2, 2024

Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but crucial to rank importance each factor terms non-linear impact on erosion rate. Hence, goal this study was perform a GSA RUSLE for assessment southern Bahia, Brazil. To meet goal, three topographic (LS factor) equations alternately implemented RUSLE, coupled with geographic information system (GIS) software and variogram response surfaces (VARSs), were used. The results showed that average rate Pardo River basin 25.02 t/ha/yr. In addition, slope angle which associated LS most sensitive parameter, followed by cover management (C support practices (P (CP factors), specific catchment area (SCA), sheet (m), erodibility (K factor), rill (n), erosivity (R factor). novelty work values parameters m n can substantially affect and, thus, estimation.

Language: Английский

Citations

0