J 2021

Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province

LIU, Mengmeng, Jiping LIU, Shenghua XU, Tao ZHOU, Yu MA et. al.

Základní údaje

Originální název

Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province

Autoři

LIU, Mengmeng, Jiping LIU (garant), Shenghua XU, Tao ZHOU, Yu MA, Fuhao ZHANG a Milan KONEČNÝ (203 Česká republika, domácí)

Vydání

International Journal of Image and Data Fusion, Taylor and Francis Ltd. 2021, 1947-9832

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10508 Physical geography

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Kód RIV

RIV/00216224:14310/21:00122321

Organizační jednotka

Přírodovědecká fakulta

DOI

UT WoS

000684463600001

Klíčová slova anglicky

Landslide susceptibility mapping; fuzzy c-means; support vector machine; Shaanxi Province

Štítky

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 8. 11. 2021 16:44, Mgr. Marie Šípková, DiS.

Anotace

V originále

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%).