Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland

Choosing appropriate landslide-controlling factors (LCFs) in landslide susceptibility mapping (LSM) is a challenging task and depends on the nature of terrain and expert knowledge and experience. Nowadays, it is very common to use digital elevation model (DEM) and DEM-derivatives, as a representation of the topographic conditions. The objective of this study is to explore topography in depth and simultaneously reduce redundant information within DEM-derivatives using principal component analysis. Moreover, this study investigates the impact of DEM-derived factors on LSM. Therefore, three various strategies were tested. The first strategy included a set of LCFs created from the four initial principal components, which were provided from DEM-derived factors. The second strategy included a set of parameters which contained additional lithological and environmental factors. The third strategy utilises the analytical hierarchy process (AHP) to assign weights to each LCF. The LSM was performed based on landslide susceptibility index. Obtained results show that 60% of existing landslides fell into high and very high susceptibility zones using first and second strategies. It proves that topographic factors play a significant role in LSM. Adding additional lithological and environmental factors to the set of LCFs did not improve the results significantly, unless the AHP was used in the third strategy. It improved results significantly; up to 70%. Results from second and third strategies highlight utility of AHP in LSM. Presented studies were performed on the area very prone to landslide occurrence in the region of Rożnów Lake, Poland.

[1]  B. Pradhan,et al.  Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models , 2012 .

[2]  R. W. Fleming,et al.  Economic Losses and Fatalities Due to Landslides , 1986 .

[3]  P. Aleotti,et al.  Landslide hazard assessment: summary review and new perspectives , 1999 .

[4]  S. Bai,et al.  GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China , 2010 .

[5]  William H. Schulz,et al.  Landslides mapped using LIDAR imagery, Seattle, Washington , 2004 .

[6]  C. Gokceoglu,et al.  GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran) , 2014, Arabian Journal of Geosciences.

[7]  L. Ayalew,et al.  Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications , 2005 .

[8]  Rubini Mahalingam,et al.  Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study) , 2016 .

[9]  N. Saadatkhah,et al.  Qualitative and quantitative landslide susceptibility assessments in Hulu Kelang area, Malaysia , 2014 .

[10]  F. Guzzetti,et al.  Landslide inventory maps: New tools for an old problem , 2012 .

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

[12]  Andrzej Borkowski,et al.  LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION , 2016 .

[13]  V. Doyuran,et al.  A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate , 2004 .

[14]  Saro Lee,et al.  Earthquake-induced landslide-susceptibility mapping using an artificial neural network , 2006 .

[15]  B. Pradhan,et al.  Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran , 2012 .

[16]  Nguyen Quoc Thanh,et al.  Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization , 2017, Landslides.

[17]  S. L. Kuriakose,et al.  Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview , 2008 .

[18]  Aykut Akgun,et al.  Landslide susceptibility assessment in the İzmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method , 2009 .

[19]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[20]  Thomas Blaschke,et al.  A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping , 2014, Comput. Geosci..

[21]  Geoffrey H. Ball,et al.  ISODATA, A NOVEL METHOD OF DATA ANALYSIS AND PATTERN CLASSIFICATION , 1965 .

[22]  Weitao Chen,et al.  Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China , 2013, Environmental Earth Sciences.

[23]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

[24]  J. McKeana,et al.  Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry , 2004 .

[25]  L. Highland,et al.  The Landslide Handbook - A Guide to Understanding Landslides , 2008 .

[26]  John P. Wilson,et al.  Terrain analysis : principles and applications , 2000 .

[27]  T. Saaty Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process , 2000 .

[28]  Christos Polykretis,et al.  GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece , 2014 .

[29]  P. K. Champati ray,et al.  Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya , 2007 .

[30]  M. Bednarik,et al.  Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania) , 2011 .

[31]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[32]  Manoj K. Arora,et al.  Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas , 2008 .

[33]  H. Abdi,et al.  Principal component analysis , 2010 .

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

[35]  A. Akgun,et al.  Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models , 2008 .

[36]  Mustafa Neamah Jebur,et al.  Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale , 2014 .

[37]  T. Saaty Fundamentals of the Analytic Hierarchy Process , 2001 .

[38]  L. Ayalew,et al.  Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan , 2004 .

[39]  Alexander Brenning,et al.  Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling , 2015, Comput. Geosci..

[40]  Paul E. Gessler,et al.  Soil-Landscape Modelling and Spatial Prediction of Soil Attributes , 1995, Int. J. Geogr. Inf. Sci..

[41]  R. Manolov,et al.  Retaining principal components for discrete variables , 2011 .

[42]  A. Akgun,et al.  Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis , 2010 .

[43]  Antoni Wójcik,et al.  LANDSLIDES MAPPING IN ROZNOW LAKE VICINITY, POLAND USING AIRBORNE LASER SCANNING DATA , 2011 .

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

[45]  T. Kavzoglu,et al.  Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm , 2015 .

[46]  P. Kayastha,et al.  Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal , 2013, Comput. Geosci..

[47]  B. Ahmed Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh , 2015, Landslides.

[48]  D. J. Chadwick,et al.  Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity , 2006 .

[49]  J. Malet,et al.  Landslide susceptibility assessment by bivariate methods at large scales: Application to a complex mountainous environment , 2007 .

[50]  T. L. Saaty A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[51]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[52]  D. P. Kanungo,et al.  An Integrated Approach for Landslide Susceptibility Mapping Using Remote Sensing and GIS , 2004 .

[53]  Davide Brambilla,et al.  A simplified early-warning system for imminent landslide prediction based on failure index fragility curves developed through numerical analysis , 2016 .

[54]  M. Turrini,et al.  An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy) , 2002 .

[55]  D. Bui,et al.  Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression , 2011 .

[56]  Azman Kassim,et al.  Susceptibility Assessment of Shallow Landslides in Hulu Kelang Area, Kuala Lumpur, Malaysia Using Analytical Hierarchy Process and Frequency Ratio , 2015, Geotechnical and Geological Engineering.

[57]  Landslide Hazards in the Polish Flysch Carpathians: Example of Łososina Dolna Commune , 2013 .

[58]  A. G. Rafek,et al.  Landslide Susceptibility Assessment using Frequency Ratio Model Applied to an Area along the E-W Highway (Gerik-Jeli) , 2011 .

[59]  Ian D. Moore,et al.  Terrain attributes: estimation methods and scale effects , 1993 .

[60]  Marko Komac,et al.  Regional landslide susceptibility model using the Monte Carlo approach– the case of Slovenia , 2012 .

[61]  Manoj K. Arora,et al.  A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas , 2006 .

[62]  A. Nonomura,et al.  GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping , 2008 .

[63]  L. Highland,et al.  The Landslide Handbook-a Guide to Understanding Landslides: A Landmark Publication for Landslide Education and Preparedness , 2013 .

[64]  K. Solaimani,et al.  Landslide Susceptibility Mapping Using Multiple Regression and GIS Tools in Tajan Basin, North of Iran , 2012 .

[65]  Jan Nyssen,et al.  Use of LIDAR‐derived images for mapping old landslides under forest , 2007 .

[66]  J. McKean,et al.  Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry , 2004 .

[67]  Pierre Soille,et al.  Morphological gradients , 1992, Electronic Imaging.

[68]  Manoj K. Arora,et al.  Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[69]  B. Pradhan,et al.  Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran , 2013, Arabian Journal of Geosciences.

[70]  Alberto González,et al.  Validation of Landslide Susceptibility Maps; Examples and Applications from a Case Study in Northern Spain , 2003 .

[71]  Candan Gokceoglu,et al.  The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity , 2005 .