Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China Article Swipe
Yang Zhang
,
Weicheng Wu
,
Yaozu Qin
,
Ziyu Lin
,
Guiliang Zhang
,
Renxiang Chen
,
Yong Song
,
Tao Lang
,
Xiaoting Zhou
,
Wenchao Huangfu
,
Penghui Ou
,
Lifeng Xie
,
Xiaolan Huang
,
Shanling Peng
,
Chongjian Shao
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi9110695
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi9110695
Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi9110695
- https://www.mdpi.com/2220-9964/9/11/695/pdf?version=1606205066
- OA Status
- gold
- Cited By
- 46
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3107040622
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3107040622Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijgi9110695Digital Object Identifier
- Title
-
Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, ChinaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-23Full publication date if available
- Authors
-
Yang Zhang, Weicheng Wu, Yaozu Qin, Ziyu Lin, Guiliang Zhang, Renxiang Chen, Yong Song, Tao Lang, Xiaoting Zhou, Wenchao Huangfu, Penghui Ou, Lifeng Xie, Xiaolan Huang, Shanling Peng, Chongjian ShaoList of authors in order
- Landing page
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https://doi.org/10.3390/ijgi9110695Publisher landing page
- PDF URL
-
https://www.mdpi.com/2220-9964/9/11/695/pdf?version=1606205066Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2220-9964/9/11/695/pdf?version=1606205066Direct OA link when available
- Concepts
-
Landslide, Hazard, Zoning, Algorithm, Geology, Lithology, Random forest, Remote sensing, Cartography, Data mining, Seismology, Computer science, Geography, Civil engineering, Artificial intelligence, Engineering, Organic chemistry, Paleontology, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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46Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 18, 2023: 6, 2022: 8, 2021: 9Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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