Abstract
The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.
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Acknowledgments
This research was funded by the Norwegian Quota scholarship. The data analysis and write-up were carried out as a part of the first author’s PhD studies at the Geomatics section, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Norway. I would like to thank Dr. Tran Tan Van, director of Vietnam Institute of Geosciences and Mineral Resources, for valuable comments.
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Appendix
Appendix
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1.
Four tiles of the Geological and Mineral Resources Map of Vietnam at the scale of 1:200.000 are: (1) The Hanoi F-48-XXVII; (2) the Ninh Binh F-48-XXXIV; (3) the Van Yen F-48-XXVII; (4) the Sam Nua F-48-XXXIII.
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2.
Characteristics of lithology groups, which were used in this study
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Group 1: Quaternary deposits: Primarily distributed in plains and river valleys, characterized by incoherent textures, diversified components, abundant material size, and essential alluvial facies.
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Group 2: Sedimentary aluminosilicate rocks and sedimentary quartz rocks: Consisting of pebbles, cobble, gravel, gritstone, sandstone, siltstone, claystone, carbonates, alternated rhyolites, dacites, andesite sediments, and tuff. Sedimentary quartz rocks consist of quartz–mica sandstone, quartzitic sandstone, and cherty shale.
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Group 3: Sedimentary carbonate rocks: consisting of limestone, dolomitized limestone, cherty limestone, clayish limestone.
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Group 4: Mafic–ultramafic magma rocks: Consisting of dunit, peridotit, pyroxenit, tremolite schist, artinolite schist, gabbro–pyroxenit, gabbro–amphibolit, gabbro–norit, gabbro–anorthosit, gabbro–diorit, gabbro–diabas, diabas, mafic bazan olivin, bazan tholeite, and bazan dolerite.
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Group 5: Acid–neutral magmatic rocks: The extrusive magmas consist of rhyolite, dacite, felsite, and andesite rocks. The intrusive granite magmas consist of plagioclase-granite, granophyre, granosyenite, granodiorite, diorite, and quartz-diorite.
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Group 6: Metamorphic rock with rich aluminosilicate component: The high-rank metamorphic rocks consist of biotite–garnet-gneiss, amphibole-biotite-lagiogneiss, biotite-amphibolite, plagioclase-migmatite, quartz-biotite schist, biotite schist… The low-rank metamorphic rocks consist of green schist, chlorite schist, sericite schist, and quartz-sericite schist
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Group 7: Metamorphic rock with rich quartz component: Consists of quartz-mica schist, quartz-sericite schist, quartzite, and sericite-quartzite.
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Bui, D.T., Lofman, O., Revhaug, I. et al. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59, 1413–1444 (2011). https://doi.org/10.1007/s11069-011-9844-2
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DOI: https://doi.org/10.1007/s11069-011-9844-2