| Autor: |
De Smedt, Florimond, Kayastha, Prabin, Dhital, Megh Raj |
| Předmět: |
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| Zdroj: |
Geosciences (2076-3263); Oct2023, Vol. 13 Issue 10, p306, 16p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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