- Title
- Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories
- Creator
- Huang, Faming; Ye, Zhou; Zhou, Xiaoting; Huang, Jinsong; Zhou, Chuangbing
- Relation
- Bulletin of Engineering Geology and the Environment Vol. 81, no. 250
- Publisher Link
- http://dx.doi.org/10.1007/s10064-022-02748-2
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2022
- Description
- Existing studies relating to landslide susceptibility prediction (LSP) either do not pay enough attentions to the continuously updated landslide inventories or use batch learning methods for LSP, resulting in the insufficient use of the entire landslide inventory. To overcome this problem, the Incremental Learning theory combined with a Bayesian Network (ILBN) model is constructed for LSP. Wencheng County of China is taken as the study area, a landslide inventory from 1985 to 2019 and 10 conditioning factors are mapped and analyzed. Then, the LSP results of the ILBN model are compared with the batch learning-based multilayer perceptron (BL-MLP) and support vector machine (BL-SVM) models. Results show that the LSP accuracies of ILBN_0 (ILBN modeling of initial landslide inventory), ILBN_1 (the first Incremental Learning model), and ILBN_2 (the second Incremental Learning model) increase gradually with the AUC value of 0.807, 0.813, and 0.835, respectively. The LSM produced by the ILBN model is more consistent with the law of landslides distribution in the study area. The mean values of ILBN_0, ILBN_1, and ILBN_2 are 0.307, 0.287, and 0.245, and the standard deviations are 0.278, 0.281, and 0.308, respectively. Meanwhile, the characteristics of LSIs in Wencheng County are in line with the actual landslides distribution with the main controlling factors of lithology, elevation, and normalized difference building indexes determined by the weighted mean method. Furthermore, the LSP results of ILBN model are superior to those of the BL-MLP and BL-SVM models. It is concluded that the ILBN model can better address the long-term, continuous LSP using the new added landslide inventory.
- Subject
- landslide susceptibility prediction; incremental learning; bayesian network; multilayer perceptron; support vector machine; remote sensing
- Identifier
- http://hdl.handle.net/1959.13/1487696
- Identifier
- uon:52223
- Identifier
- ISSN:1435-9529
- Language
- eng
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