- Title
- Uncertainties of landslide susceptibility prediction: Influences of different spatial resolutions, machine learning models and proportions of training and testing dataset
- Creator
- Huang, Faming; Teng, Zuokui; Guo, Zizheng; Catani, Filippo; Huang, Jinsong
- Relation
- Rock Mechanics Bulletin Vol. 2, Issue 1, no. 100028
- Publisher Link
- http://dx.doi.org/10.1016/j.rockmb.2023.100028
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2023
- Description
- This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction (LSP), namely the spatial resolution, proportion of model training and testing datasets and selection of machine learning models. Taking Yanchang County of China as example, the landslide inventory and 12 important conditioning factors were acquired. The frequency ratios of each conditioning factor were calculated under five spatial resolutions (15, 30, 60, 90 and 120 m). Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets (9:1, 8:2, 7:3, 6:4 and 5:5), and four typical machine learning models were applied for LSP modelling. The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty. With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5, the modelling accuracy gradually decreased, while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased. The sensitivities of the three uncertainty issues to LSP modeling were, in order, the spatial resolution, the choice of machine learning model and the proportions of training/testing datasets.
- Subject
- landslide susceptibility prediction; uncertainty analysis; machine learning models; conditioning factors; spatial resolution; proportions of training and testing dataset
- Identifier
- http://hdl.handle.net/1959.13/1499144
- Identifier
- uon:54625
- Identifier
- ISSN:2773-2304
- Language
- eng
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