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

Florimond De Smedt, Prabin Kayastha, Dhital Megh Raj

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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 prob- ability 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.
Originele taal-2English
Aantal pagina's16
Nummer van het tijdschrift10
StatusPublished - okt 2023

Bibliografische nota

Publisher Copyright:
© 2023 by the authors.


  • Keywords: landslide; naïve Bayes classification; semi-naïve Bayes classification; weights-of-evidence; logistic regression; landslide susceptibility mapping; Kulekhani River Basin; Nepal

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