Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation

Abstract

The phenomenon of land degradation is a serious environmental issue that affects many countries worldwide, particularly in the developing countries of sub-tropical regions like India. The assessment of land degradation is necessary for designing a mitigation plan that will reduce the adverse effects of land degradation. Recently, sensitivity models for analyzing land degradation have become a popular scientific tool for determining the spatial characteristics of this complicated environmental phenomenon. The objective of the current study is to prepare land degradation susceptibility maps for the gravely undulating red and lateritic agro-climatic zones (ACZ) of the Eastern plateau, India using hybrid techniques, i.e., integration of K-Fold cross-validation (CV) and machine learning algorithms of Reduced Error Pruning Tree (REPTree) and the ensemble of Bagging-REPTree and Boosting-REPTree. For the modelling purpose, sixteen independent land degradation conditioning factors were selected based on a multi-collinearity test, and dependent factors, i.e., gully and ravine points, were collected from published reports and field investigations. The evaluation result of the models indicates that Boosting-REPTree is the most optimal in prediction analysis, as the area under the curve (AUC) of training and validation is 0.944 and 0.928, respectively, in K-Fold 1 followed by Bagging-REPTree and REPTree. As a result, this study suggested that the ensemble of the Boosting-REPTree model can be applied as a new potential method for spatial prediction of land degradation in future research. The study also revealed that ex-situ plant species had been adopted to control soil erosion. Still, it is considered a false measure as ex-situ plant species cannot prevent erosion to an optimal level. Overall, a land degradation prevention planning map has also been suggested to measure soil erosion.