The selection of non-landslide samples has a great impact on the machine learning modelling for landslide susceptibility prediction (LSP). This study presents a novel framework for studying the uncertainty of non-landslide samples selection on the LSP results through the slope unit-based machine learning models. In this framework, the non-landslide samples are randomly selected from the non-landslide areas by multiple times (N = 1, 10, 100, 500, 1000, 5000) to construct LSP models and calculate N types of landslide susceptibility indexes (LSIs). Afterwards, the statistical analysis is used to represent the uncertainty of LSIs under each non-landslide selection. The maximum probability analysis (MPA) is applied to reduce the uncertainty of non-landslide samples selection in LSP, which calculates the probability of N types of LSIs falling into very high, high, moderate, low and very low landslide susceptibility levels and selects the optimal landslide susceptibility level with the highes...

Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models

Bhuyan K.;Meena S. R.;Catani F.
2023

Abstract

The selection of non-landslide samples has a great impact on the machine learning modelling for landslide susceptibility prediction (LSP). This study presents a novel framework for studying the uncertainty of non-landslide samples selection on the LSP results through the slope unit-based machine learning models. In this framework, the non-landslide samples are randomly selected from the non-landslide areas by multiple times (N = 1, 10, 100, 500, 1000, 5000) to construct LSP models and calculate N types of landslide susceptibility indexes (LSIs). Afterwards, the statistical analysis is used to represent the uncertainty of LSIs under each non-landslide selection. The maximum probability analysis (MPA) is applied to reduce the uncertainty of non-landslide samples selection in LSP, which calculates the probability of N types of LSIs falling into very high, high, moderate, low and very low landslide susceptibility levels and selects the optimal landslide susceptibility level with the highes...
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3470609
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