Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F21%3A00122321" target="_blank" >RIV/00216224:14310/21:00122321 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1080/19479832.2021.1961316" target="_blank" >https://doi.org/10.1080/19479832.2021.1961316</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/19479832.2021.1961316" target="_blank" >10.1080/19479832.2021.1961316</a>
Alternative languages
Result language
angličtina
Original language name
Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province
Original language description
The quality of "non-landslide' samples data impacts the accuracy of geological hazard risk assessment. This research proposed a method to improve the performance of support vector machine (SVM) by perfecting the quality of `non-landslide' samples in the landslide susceptibility evaluation model through fuzzy c-means (FCM) cluster to generate more reliable susceptibility maps. Firstly, three sample selection scenarios for `non-landslide' samples include the following principles: 1) select randomly from low-slope areas (scenario-SS), 2) select randomly from areas with no hazards (scenarioRS), 3) obtain samples from the optimal FCM model (scenario-FCM), and then three sample scenarios are constructed with 10,193 landslide positive samples. Next, we have compared and evaluated the performance of three sample scenarios in the SVM models based on the statistical indicators such as the proportion of disaster points, density of disaster points precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). Finally, The evaluation results show that the `non-landslide' negative samples based on the FCM model are more reasonable. Furthermore, the hybrid method supported by SVM and FCM models exhibits the highest prediction efficiency. Scenario FCM produces an overall accuracy of approximately 89.7% (AUC), followed by scenario-SS (86.7%) and scenario-RS (85.6%).
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10508 - Physical geography
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Image and Data Fusion
ISSN
1947-9832
e-ISSN
1947-9824
Volume of the periodical
12
Issue of the periodical within the volume
4
Country of publishing house
GB - UNITED KINGDOM
Number of pages
18
Pages from-to
349-366
UT code for WoS article
000684463600001
EID of the result in the Scopus database
2-s2.0-85112263160