Naïve and Semi-Naïve Bayesian Classification of Landslide Susceptibility Applied to the Kulekhani River Basin in Nepal as a Test Case

Abstract

Naïve Bayes classification is widely used for landslide susceptibility analysis, especially in the form of weights-of-evidence. However, when significant conditional dependence is present, the probabilities derived from weights-of-evidence are biased, resulting in an overestimation of landslide susceptibility. As a solution, this study presents a semi-naïve Bayesian method for landslide susceptibility mapping by combining logistic regression with weights-of-evidence. The utility of the method is tested by application to a case study in the Kulekhani River Basin in Central Nepal. The results show that the naïve Bayes approach with weights-of-evidence overpredicts the posterior probability of landslide occurrence by a factor of about two, while the semi-naïve Bayes approach, which uses logistic regression with weights-of-evidence, is unbiased and has more discriminatory power for landslide susceptibility mapping. In addition, the semi-naïve Bayes approach can statistically distinguish the main factors that promote landslides and allows us to estimate the model uncertainty by calculating the standard error of the predictions.