Modeling livelihood vulnerability in erosion and flooding induced river island in Ganges riparian corridor, India

River island (locally known as charland) in river Ganges from Rajmahal hill to Farakka barrage of India is now under human habitat dominated by the environmentally evicted people triggered by bank erosion, but these are under different physical vulnerability like bank erosion, flooding etc. Considering the physical inconvenience, and inaccessibility, the present work intended to model the livelihood vulnerability state (LVS) by using advance machine learning algorithms, like Artificial neural network (ANN), Random forest (RF), Random subspace (RS) and Support vector machine (SVM). For LVS modelling, field and remote sensing based 26 parameters were selected. We classified the parameters as exposure (11 parameters), sensitivity (4 parameters) and adaptive capacity (11 parameters). We modelled LVS for overall condition, exposure, adaptive capacity, and sensitivity. Application of these algorithms in this field is unique and its robustness and precision in result is highly satisfactory. LVS models clearly identified 39% to 53% of areas having high to very high vulnerability and these are located at the edge of the charlands. Among the models, SVM outperformed as per the result of accuracy assessment. Therefore, it can be treated as a representative algorithm for LVS modelling. Among the 26 parameters, bank erosion, unhygienic condition, and Below Poverty Level (BPL) household parameters were found as highly dominating based on the findings of information and gain ratio. The correlation with LVS and three individual models (exposure, adaptive capacity, and sensitivity) exhibited that the Exposure model was highly correlated (r = 0.87) with high statistical significance (0.001 level).