GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models

This study aimed to produce landslide susceptibility maps using frequency ratio (FR) and evidential belief function (EBF) models based on GIS for Gongliu County, China. For this purpose, a detailed landslide inventory map was prepared and 12 landslide conditioning factors were considered; these factors were as follows: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, the normalized difference vegetation index, rainfall, the topographic wetness index (STI), distance from roads, distance from rivers, and lithology. The landslide inventory map was prepared from published sources and aerial photographs, supported by field work. A total of 233 landslides were identified and mapped in the study area. Out of which, 70 % landslides were applied for establishing the model and 30 % landslides were used to validate the model. GIS software was used to analyze landslide conditioning factors and to map landslide susceptibility. Landslide susceptibility maps were prepared using FR and EBF models based on ArcGIS 10.0 and classified into five susceptibility zones: very low, low, moderate, high, and very high. Finally, in order to validate the accuracy of the landslide susceptibility maps produced from two models, the area under curve approach was applied. The validation results showed that the maps using FR and EBF models have a success rate of 82.41 and 79.69 %, respectively. Similarly, the maps using FR and EBF models have a predictive rate of 77.26 and 68.79 %, respectively. Based on the results, both landslide susceptibility maps produced by the two models have a high accuracy and will be helpful for land-use planning in the study area.

[1]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[2]  Aykut Akgün,et al.  A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods , 2013, Natural Hazards.

[3]  S. Reis,et al.  A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics , 2011 .

[4]  Tamer Topal,et al.  GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey) , 2012, Environmental Earth Sciences.

[5]  Mrinal K. Ghose,et al.  Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India , 2014, Natural Hazards.

[6]  Zhaohua Chen,et al.  Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada , 2007 .

[7]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[8]  Saro Lee,et al.  Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea , 2013 .

[9]  H. Pourghasemi,et al.  Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances , 2013, Natural Hazards.

[10]  B. Pradhan,et al.  Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models , 2015, Geosciences Journal.

[11]  No-Wook Park,et al.  Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis , 2011 .

[12]  B. Pradhan,et al.  Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya , 2014, Arabian Journal of Geosciences.

[13]  Collapse Landslide and Mudslides Hazard Zonation , 2014 .

[14]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[15]  F. Mancini,et al.  GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy , 2010 .

[16]  Andrea G. Fabbri,et al.  Validation of Spatial Prediction Models for Landslide Hazard Mapping , 2003 .

[17]  B. Pradhan,et al.  Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran , 2012, Natural Hazards.

[18]  R. Nagarajan,et al.  Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions , 2000 .

[19]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[20]  B. Pradhan,et al.  Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam , 2013, Natural Hazards.

[21]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling , 2010, Environ. Model. Softw..

[22]  Mustafa Neamah Jebur,et al.  Spatial landslide hazard assessment along the Jelapang Corridor of the North-South Expressway in Malaysia using high resolution airborne LiDAR data , 2015, Arabian Journal of Geosciences.

[23]  Saro Lee,et al.  Statistical analysis of landslide susceptibility at Yongin, Korea , 2001 .

[24]  M. Komac.  A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia , 2006 .

[25]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[26]  C. F. Lee,et al.  Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong , 2001 .

[27]  Biswajeet Pradhan,et al.  Application of an evidential belief function model in landslide susceptibility mapping , 2012, Comput. Geosci..

[28]  V. Doyuran,et al.  Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey , 2004 .

[29]  H. A. Nefeslioglu,et al.  Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey) , 2008 .

[30]  B. Pradhan,et al.  Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya , 2012, Natural Hazards.

[31]  S. Anbazhagan,et al.  Landslide susceptibility mapping along Kolli hills Ghat road section (India) using frequency ratio, relative effect and fuzzy logic models , 2015, Environmental Earth Sciences.

[32]  E. Carranza,et al.  Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines , 2003 .

[33]  B. Pradhan,et al.  Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models , 2007 .

[34]  Chiara Lepore,et al.  Rainfall-induced landslide susceptibility zonation of Puerto Rico , 2012, Environmental Earth Sciences.

[35]  Suzana Dragicevic,et al.  GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments , 2015 .

[36]  Vahid Nourani,et al.  Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models , 2014, Natural Hazards.

[37]  Chong Xu,et al.  The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling , 2012, Environmental Earth Sciences.

[38]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

[39]  M. H. Abokharima,et al.  Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS , 2014, Natural Hazards.

[40]  A. Ozdemir,et al.  A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey , 2013 .

[41]  B. Pradhan.  Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches , 2010 .

[42]  M. Kobiyama,et al.  Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil , 2014, Journal of Soils and Sediments.

[43]  T. Kavzoglu,et al.  An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district , 2015, Natural Hazards.

[44]  Soyoung Park,et al.  Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea , 2013, Environmental Earth Sciences.

[45]  Weitao Chen,et al.  Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China , 2014 .

[46]  Mukta Sharma,et al.  GIS-based landslide hazard zonation: a case study from the Parwanoo area, Lesser and Outer Himalaya, H.P., India , 2008 .

[47]  Paraskevas Tsangaratos,et al.  A Geographical Information System (GIS) Based Probabilistic Certainty Factor Approach in Assessing Landslide Susceptibility: The Case Study of Kimi, Euboea, Greece , 2015 .

[48]  Biswajeet Pradhan,et al.  Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of , 2012 .

[49]  Á. Felicísimo,et al.  Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study , 2013, Landslides.