the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Oct 2023
Regional landslide susceptibility assessment based on Inter.iamb-Tabu algorithm
Chao Yin
Xixuan Zhang
Xuebing Ma
Xinliang Liu
Shufeng Li
Abstract. Due to the great differences in geological environment characteristics and landslide disaster mechanism in different regions, the logical structure of each mathematical model is also different. It can only be determined through comparative research. Four improved algorithms based on Bayesian networkwere verified, and the error index was introduced to determine the algorithm with the best modeling effect. The landslide susceptibility probability of 774570 grids in Boshan District was calculated, and the landslide susceptibility distribution map of Boshan District was plotted. Based on the spatial superposition and grid calculator function of GIS, the landslide susceptibility assessment results of each model were compared.
- Preprint
(1876 KB) - Metadata XML
- BibTeX
- EndNote
Chao Yin et al.
Status: open (until 28 Dec 2023)
-
RC1: 'Comment on nhess-2023-153', Anonymous Referee #1, 10 Nov 2023
reply
The submitted manuscript presents a landslide susceptibility assessment using four improved algorithms based on the Bayesian network. The paper is poorly written and needs to be largely reworked. The whole landslide susceptibility analysis is non-representative, from the selection of landslide causal factors to the definition of the landslide sample for model training and verification. The paper lacks an objective presentation of the landslide susceptibility maps and verification results (only one AUC result is mentioned and shown in the manuscript). The comparison of the obtained maps is interesting, but it is not representative and it is not clear why one of the models was accepted as the most accurate and chosen as a reference model for the verification of the other models. The paper lacks separate discussion and conclusion chapters. In my opinion, the manuscript does not reach the required quality standard of this journal.
Citation: https://doi.org/10.5194/nhess-2023-153-RC1
Chao Yin et al.
Chao Yin et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 194 | 33 | 4 | 231 | 4 | 5 |
- HTML: 194
- PDF: 33
- XML: 4
- Total: 231
- BibTeX: 4
- EndNote: 5
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1