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Soil erosion-induced land degradation is susceptible to climate change, specifically in the sub-tropical third world countries. Simulations of 21st century climate change in India predict notable variation in rainfall that causes soil erosion-induced land degradation. Land degradation susceptibility modelling of the red and lateritic agro-climatic zone of Bengal (Eastern India) has been prepared using random forest (RF), support vector machine (SVM) and extreme gradient boost (XGBoost) algorithms. Assessment of models using validation data in AUC-ROC revealed that XGBoost (0.909 and r = 0.91) is the most optimal followed by SVM (0.881 and r = 0.87) and RF (0.879 and r = 0.85). Furthermore, future land degradation risk dynamics were assessed through Coupled Model Intercomparison Project six (CMIP6) down-scale-based ensembles of nine global climate models (GCMs) on four SSPs scenarios. The combination of deep learning along with climate modelling should be useful to enhance the result more precisely.

Threats of soil erosion under CMIP6 SSPs scenarios: an integrated data mining techniques and geospatial approaches