Uncertainties in landslide susceptibility prediction: Influence rule of different levels of errors in landslide spatial position

Abstract

The accuracy of landslide susceptibility prediction (LSP) mainly depends on the precision of the landslide spatial position. However, the spatial position error of landslide survey is inevitable, resulting in considerable uncertainties in LSP modeling. To overcome this drawback, this study explores the influence of positional errors of landslide spatial position on LSP uncertainties, and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error. This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example. The 30–110 m error-based multilayer perceptron (MLP) and random forest (RF) models for LSP are established by randomly offsetting the original landslide by 30, 50, 70, 90 and 110 m. The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics. Finally, a semi-supervised model is proposed to relieve the LSP uncertainties. Results show that: (1) The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors, and are lower than those of original data-based models; (2) 70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices, thus original landslides with certain position errors are acceptable for LSP; (3) Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Keywords

• Landslide susceptibility prediction
• Random landslide position errors
• Uncertainty analysis
• Multi-layer perceptron
• Random forest
• Semi-supervised machine learning