Gully Erosion Susceptibility Using Advanced Machine Learning Method in Pathro River Basin, India

The concept of gully erosion susceptibility has received more focus in recent years, and the attention has been drown by researchers for the implementation of policy and practices. Soil erosion through gully development is a natural geomorphic process that controlled by human activities and highly effected environmental quality, ecosystem, natural resources, and agricultural activities; and it promotes hazards. So, gully erosion management is a key attempt for sustainable land use practices to assess and monitor the soil quality. In this work, the researchers try to employ a gully erosion susceptibility model along with management strategies and its controlling factors in Pathro River Basin, India. Authors follow a well-accepted machine learning-based boosted regression tree (BRT) model for the assessment of urgent management within the study area. Twelve predisposing factors were used here for the development of susceptibility map to find the areas that urgently required to take a robust management policy. The model depicts high prediction capacity with  a strong area under the curve (AUC is 87.40%). Finally, the dynamic nature of ecosystem service value (ESVs) and its sensitivity to land use have been examined by implementing an elasticity indicator. Badland areas were converted to forestland during 2010–2020 to manage the land degradation, but in some areas the gully erosion processes and their increasing trend were found due to unplanned land use practices. Also, the causes of erosive agents were evaluated by fitted function. For this study basin, forest is pivotal land use for both management of land degradation and ESVs so, it should be managed and conserved by afforestation programmes. The outcome of this work provides a new window for policy makers to initiate appropriate dimension about the land degradation and ecosystem management in prioritized areas of humid tropics.