RESUMO
Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with analytical hierarchy process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological, and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP-based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF), and support vector machine (AHP-SVM) model. The model performances were assessed through an area under the receiver operating characteristic (AUC). The AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977), and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.