Assessment of Slope Instability in a Hilly Terrain: A Logistic Regression and Random Forest Based Approach

Landslides are one such spontaneous natural disaster which has the potential to effect human lives as well as economic property in any region. The continuous exploration of qualitative and quantitative approaches augmented with RS-GIS methodologies for assessing landslides have given new dimensions to the area of research. The main obstacle of these methodologies remains in the fact that the hypothesis needs to be taken as true even before the analysis. To overcome the same, with the development of machine learning methodologies, the exhibits are found to be more objective in terms of quantification as well as the quality of research. The current assessment was aimed to evaluate the execution of two machine learning approaches namely; logistic regression and random forest for the assessment of slope instability in Darjeeling Himalayas. Eight geo-environmental factors were taken into consideration for the said assessment. The susceptibility models prepared with the said approaches were substantiated using receiver operating characteristics curves. The assessed accuracies were 76.8% and 79.7% for logistic regression and random forest respectively with the dataset(training and validation), the accuracies measured were 76.8% and 77.7% respectively.