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Landslide Susceptibility Mapping along Bhalubang–Shiwapur Area of Mid-Western Nepal Using frequency ratio and Conditional Probability Models

引用

Journal of Mountain Science 2014年第5期11卷 1266-1285页

作者： Amar Deep REGMI Kohki YOSHIDA Hamid Reza POURGHASEMI Megh Raj DHITAL Biswajeet PRADHAN Faculty of Technology Nepal Academy of Science and Technology Department of Geology Faculty of Science Shinshu University College of Natural Resources & Marine Sciences Tarbiat Modares University (TMU) Noor 14115-335 Tehran Iran Central Department of Geology Tribhuvan University Faculty of Engineering Department of Civil Engineering Geosptial Information Science Research Centre (GISRC) University of Putra Malaysia Serdang Selangor Darul Ehsan 43400 Malaysia

Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang–Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping(LSM) of this region was carried out using frequency ratio(FR) and weights-of-evidence(W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226(70%) were randomly selected for model training and the remaining 106(30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified andcompared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region(W of E model(success rate = 83.39%, prediction rate = 79.59%); FR model(success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.

关键词： landslides frequency ratio weights-ofevidence gis himalaya

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