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Loess slide susceptibility assessment using frequency ratio model and artificial neural network

Qiu Haijun, Cui Peng, Amar Deep Regmi, Hu Sheng and Hao Junqing
Loess slide susceptibility assessment using frequency ratio model and artificial neural network
Quarterly Journal of Engineering Geology and Hydrogeology (July 2018) 52 (1): 38-45

Abstract

Landslide susceptibility assessment is essential for disaster management. The aim of this study is to select a reliable and accurate model for loess slide susceptibility assessment. We use a frequency ratio model and artificial neural network to develop loess slide susceptibility maps. We analysed the relationships between loess slide frequency and conditioning factors including elevation, slope gradient, aspect, profile curvature, thickness of loess, rainfall, topographic wetness index, valley depth, distance to rivers and land use. We developed a landslide inventory consisting of 223 loess slides by the interpretation of remote sensing images from earlier published or unpublished reports and from intensive field surveys. From these 223 loess slides, 178 (80%) were selected for training the models and the remaining 45 (20%) slides were used for validating the developed models. The validation was carried out by using receiver operating characteristic (ROC) curves. From the analysis, it is seen that both the frequency ratio model and artificial neural network performed equally well, although the frequency ratio method is much easier to apply. The loess slide susceptibility maps can be used for land use planning and risk mitigation in loess terrain.


ISSN: 1470-9236
EISSN: 2041-4803
Serial Title: Quarterly Journal of Engineering Geology and Hydrogeology
Serial Volume: 52
Serial Issue: 1
Title: Loess slide susceptibility assessment using frequency ratio model and artificial neural network
Affiliation: Northwest University, Shaanxi Laboratory of Earth Surface System and Environmental Carrying Capacity, Xian, China
Pages: 38-45
Published: 20180730
Text Language: English
Publisher: Geological Society of London, London, United Kingdom
References: 49
Accession Number: 2019-001742
Categories: Environmental geologyGeomorphology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. geol. sketch maps
N31°40'00" - N39°30'00", E106°00'00" - E111°19'60"
Secondary Affiliation: Chinese Academy of Sciences, Institute of Mountain Hazards and Environment, CHN, ChinaXi'an University of Finance and Economics, CHN, China
Country of Publication: United Kingdom
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data from The Geological Society, London, London, United Kingdom
Update Code: 201903
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