期刊
CATENA
卷 202, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.catena.2021.105250
关键词
Landslide susceptibility prediction; Uncertainty analysis; Frequency ratio; Data-based models; Random forest; Decision tree
资金
- National Natural Science Foundation of China [41807285, 41867036, 41972280]
- National Science Foundation of Jiangxi Province, China [20192BAB216034, 20192BBG70078]
- Postdoctoral Research Science Foundation of China [2019M652287, 2020T130274]
- Jiangxi Provincial Postdoctoral Science Foundation [2019KY08]
This study investigates the influences of different attribute interval numbers and data-based models on the accuracy of landslide susceptibility prediction. Results demonstrate that the prediction accuracy gradually increases with the increase in attribute interval numbers for a specific model, and the random forest model has the highest prediction accuracy as the attribute interval numbers increase.
This paper aims to explore the influences of different attribute interval numbers (AINs) in the frequency ratio (FR) analysis of continuous environmental factors and the influences of different data-based models on the uncertainties of landslide susceptibility prediction (LSP). Taking Ningdu County of China as study area, 446 landslides and nine environmental factors are first acquired. Then the FR values of environmental factors under 6 different AINs (4, 6, 8, 12, 16 and 20) and 6 different data-based models (FR model, grey relational degree (GRD), logistic regression (LR), multilayer perceptron (MLP), C5.0 decision tree (C5.0 DT) and random forest (RF)) are set to 36 different conditions. Finally, the LSP results with uncertainties under all conditions are discussed. Results show that: 1) For a certain model, the LSP accuracy gradually increases with the AINs increasing from 4 to 8, and then the increase rate decreases until the accuracy is stable with the AINs increasing from 8 to 20; 2) For a certain AIN, the LSP accuracy of RF is higher than that of C5.0 DT, followed by the MLP, LR, FR and GRD; 3) The LSP accuracy is highest under an AIN of 20 and RF and is satisfied under an AIN of 8 and RF, while is the lowest under an AIN of 4 and GRD; 4) The landslide susceptibility indexes (LSIs) under AINs of 4, 6 and 12 are significantly different from the other AINs, and the LSIs calculated by the C5.0 DT and RF are significantly different compared to the other models; 5) The mean values and standard deviations of LSIs calculated by the MLP, C5.0 DT and RF models are relatively smaller and larger, respectively, than those of the other models, indicating that the LSIs calculated by these models are more consistent with the actual landslide distribution features.
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