- Title
- Uncertainties of Landslide Susceptibility Prediction: Different Attribute Interval Divisions of Environmental Factors and Different Data-Based Models
- Creator
- Huang, Faming; Ye, Zhou; Yao, Chi; Li, Yuanyao; Yin, Kunlong; Huang, Jinsong; Jiang, Qinghui
- Relation
- Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences Vol. 45, Issue 12, p. 4535-4549
- Publisher Link
- http://dx.doi.org/10.3799/dqkx.2020.247
- Publisher
- Zhongguo Dizhi Daxue,China University of Geosciences
- Resource Type
- journal article
- Date
- 2020
- Description
- The attribute interval numbers (AIN) in the frequency ratio analysis of continuous environmental factors and the landslide susceptibility model are two important uncertainties affecting the results of landslide susceptibility prediction (LSP). To study the effects of the two uncertain factors on the change rules of LSP, taking Shangyou County of Jiangxi Province, China, as study area, the AIN values of the continuous environmental factors are respectively set to be 4, 8, 12, 16 and 20. Meanwhile, five different data-based models (analytic hierarchy process (AHP), logistic regression (LR), BP neural network (BPNN), support vector machines (SVM) and random forests (RF)) are selected as LSP models. Hence, there are a total of 25 types of different calculation conditions for LSP. Finally, the accuracy and uncertainties of LSP are analyzed. The results show that: (1) For a certain model, the LSP accuracy gradually increases with the AIN value increasing from 4 to 8, then slowly increases to a stable level with AIN increasing from 8 to 20; (2) For a certain AIN, the LSP accuracy of the RF model is higher than SVM, followed by the BPNN, LR and AHP models; (3) Under all the 25 calculation conditions, the prediction accuracy of AIN=20 and RF model is the highest while that of AIN=4 and AHP model is the lowest, and the modeling efficiency and accuracy of AIN=8 and RF model are very high;(4) The landslide susceptibility indexes calculated by the higher AIN and more advanced machine learning models are more consistent with the actual distribution features of landslide probability and have relatively lower uncertainties. It can be concluded that an efficient and relatively accurate LSP model can be built under the condition of AIN value of 8 and RF model.
- Subject
- landslide susceptibility prediction; uncertainty analysis; frequency ratio; attribute interval division; data-based models; engineering geology
- Identifier
- http://hdl.handle.net/1959.13/1445979
- Identifier
- uon:42723
- Identifier
- ISSN:1000-2383
- Language
- eng
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