article · Journal of African Earth Sciences
Gully erosion threatens socioeconomic stability and sustainable development across the globe. To assess this environmental danger, seven machine learning algorithms were evaluated to map gully erosion susceptibility in Morocco's Tensift catchment and Haouz plain. The assessment combined gully inventories, Sentinel satellite imagery, and a Digital Surface Model, selecting eighteen topographical, geomorphological, environmental, and hydrological predictors through multicollinearity analyses. The results indicate that 28.18 percent of the Tensift catchment faces very high erosion risk, while in the Haouz plain, 7.84 percent is categorized as very high risk. Model performance evaluation based on sensitivity, specificity, precision, and accuracy identified XGBoost and KNN as the most effective approaches, achieving area under the ROC curve values of 0.96 and 0.93 respectively. These spatial predictions offer structured evidence to inform land conservation and mitigation strategies.
Gully erosion damages productive land, undermines infrastructure, and complicates water management. By identifying which predictive models perform best and mapping exact areas at risk, this research provides land managers and policymakers with targeted data. This allows regional authorities to concentrate scarce conservation resources and erosion control measures directly on the most vulnerable landscape zones.
The models could be integrated into decision-support tools, spatial planning platforms, or environmental consulting services for watershed management and land degradation prevention. Primary users include regional land authorities, agricultural planners, and environmental agencies designing local to national mitigation programmes. As the research demonstrates validated predictive performance using catchment data, it represents applied and tested methodology ready for pilot implementation in land planning workflows.
AI-generated from the published abstract. Always read the original work before citing.
Gully erosion is a widespread environmental danger, threatening global socio-economic stability and sustainable development. This study comprehensively applied seven machine learning (ML) models including SVM, KNN, RF, XGBoost, ANN, DT, and LR, and evaluated gully erosion susceptibility in the Tensift catchment and predict it within the Haouz plain, Morocco. To ensure the reliability of the findings, the study employed a robust combination of gully erosion inventory, sentinel images, and Digital Surface Model. Eighteen predictors, encompassing topographical, geomorphological, environmental, and hydrological factors, were selected after multicollinearity analyses. The gully erosion susceptibility of the study revealed that approximately 28.18% of the Tensift catchment is at a very high risk of erosion. Furthermore, 15.13% and 31.28% of the catchment are categorized as low and very low respectively. These findings extend to the Haouz plain, where 7.84% of the surface area are very highly risking erosion, while 18.25% and 55.18% are characterized as low and very low risk areas. To gauge the performance of the ML models, an array of metrics including specificity, precision, sensitivity, and accuracy were employed. The study highlights XGBoost and KNN as the most promising models, achieving AUC ROC values of 0.96 and 0.93 in the test phase. The remaining models namely RF (AUC ROC = 0.89), LR (AUC ROC = 0.80), SVM (AUC ROC = 0.81), DT (AUC ROC = 0.86), and ANN (AUC ROC = 0.78), also displayed commendable performance. The novelty of this research is its innovative approach to combat gully erosion through cutting edge ML models, offering practical solutions for watershed conservation, sustainable management, and the prevention of land degradation. These insights are invaluable for addressing the challenges posed by gully erosion within the region, and beyond its geographical boundaries and can be used for defining appropriate mitigation strategies at local to national scale.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.jafrearsci.2024.105229
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
