Landslide susceptibility mapping using geographically-weighted principal component analysis

Landslide susceptibility mapping (LSM) documents the extent of probable landslide events in a region to investigate the distribution, pattern, recurrence and statistics of slope failure and consequent mass movement. Similar to other analyses of quantitative sources of spatial data, LSM sometimes uses principal component analysis (PCA), a form of multivariate statistical analysis. This approach helps identify susceptibility by grouping locations or by measuring the variation between groups. The present study outlines the principles and examines the capability of the proposed methodology for landslide mapping, considers optimized shapes for spatial units, estimates an efficient kernel size using alternating least squares (ALS) analysis confirmed by cross-validation, and uses geographically-weighted principal component analysis (GWPCA) to calculate landslide susceptibility using a fuzzy gamma operator. RMSE and PBIAS statistical estimators were then used to assess operational efficiency of all LSMs using fuzzy gamma operators (0.1 to 0.9). ROC curves were drawn for the best result for LSM using a landslide inventory containing 82 landslide points, with an area under curve of 0.889. The new tools can improve the quality of landslide-related analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluation.

[1]  Alberto Carrara,et al.  Multivariate models for landslide hazard evaluation , 1983 .

[2]  C.J.H. Mann,et al.  Geophysical Applications of Artificial Neural Networks and Fuzzy Logic , 2006 .

[3]  C. Gokceoğlu,et al.  Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey) , 2004 .

[4]  Piermaria Corona,et al.  Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems , 2008 .

[5]  Bakhtiar Feizizadeh,et al.  Landslide Risk Assessment Based on GIS Multi-Criteria Evaluation: A Case Study in Bostan-Abad County, Iran , 2011 .

[6]  Colin P.D. Birch,et al.  Rectangular and hexagonal grids used for observation, experiment and simulation in ecology , 2007 .

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

[8]  Azriel Rosenfeld,et al.  Connectivity in Digital Pictures , 1970, JACM.

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

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

[11]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

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

[13]  Kyoji Sassa,et al.  The influence of intense rainfall on the activity of large-scale crystalline schist landslides in Shikoku Island, Japan , 2005 .

[14]  Zargham Mohammadi,et al.  GIS-Based Landslide Susceptibility Mapping by AHP Method, A Case Study, Dena City, Iran , 2012 .

[15]  Cristiano Ballabio,et al.  Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy , 2012, Mathematical Geosciences.

[16]  A. Yalçın.  GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations , 2008 .

[17]  M. Kanevski,et al.  GIS-based landslide susceptibility mapping with comparisons of results from machine learning methods process versus logistic regression in Bailongjiang river basin, China , 2008 .

[18]  David C. Wheeler.  Visualizing and Diagnosing Coefficients from Geographically Weighted Regression Models , 2010 .

[19]  M. Marjanović,et al.  Landslide susceptibility assessment using SVM machine learning algorithm , 2011 .

[20]  Abbas Alimohammadi,et al.  A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping , 2010, Comput. Geosci..

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

[22]  Biswajeet Pradhan,et al.  An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm , 2012, Comput. Geosci..

[23]  P. Reichenbach,et al.  Estimating the quality of landslide susceptibility models , 2006 .

[24]  A. Brenning,et al.  Integrating physical and empirical landslide susceptibility models using generalized additive models , 2011 .

[25]  A. Ozdemir.  Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir, Turkey) , 2011 .

[26]  B. Neuhäuser,et al.  GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model , 2012, Landslides.

[27]  D. Wheeler.  Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression , 2007 .

[28]  Mapas de susceptibilidad de deslizamientos basados en GIS aplicados a la planificación natural y urbanística en Trikala, Grecia Central , 2009 .

[29]  B. Pradhan,et al.  Landslide Susceptibility Mapping Using a Spatial Multi Criteria Evaluation Model at Haraz Watershed, Iran , 2012 .

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

[31]  S. Sorooshian,et al.  Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data , 1996 .

[32]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[33]  A. Páez,et al.  Geographically Weighted Regression , 2021, Handbook of Regional Science.

[34]  P. Reichenbach,et al.  GIS techniques and statistical models in evaluating landslide hazard , 1991 .

[35]  John C. Davis,et al.  Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA , 2003 .

[36]  Christopher D. Lloyd,et al.  Analysing population characteristics using geographically weighted principal components analysis: A case study of Northern Ireland in 2001 , 2010, Comput. Environ. Urban Syst..

[37]  Brenden E. McNeil,et al.  GIS and biogeochemical models for examining the legacy of forest disturbance in the Adirondack Park, NY, USA , 2006 .

[38]  Majid Shadman Roodposhti,et al.  PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping , 2014, Natural Hazards.

[39]  A. Shakoor,et al.  A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses , 2010 .

[40]  David C. Wheeler,et al.  Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso , 2009 .

[41]  Chao Li,et al.  Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer , 2012, Comput. Math. Methods Medicine.

[42]  M. Rossi,et al.  Rainfall thresholds for the initiation of landslides in central and southern Europe , 2007 .

[43]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

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

[45]  D. Kalivas,et al.  Mapas de susceptibilidad de deslizamientos basados en GIS aplicados a la planificación natural y urbanística en Trikala, Grecia Central , 2009 .

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

[47]  Rong Jin,et al.  Distance Metric Learning: A Comprehensive Survey , 2006 .

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

[49]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[50]  F. Smedt,et al.  Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal , 2012, Natural Hazards.

[51]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[52]  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 .

[53]  N. Ray,et al.  Subjective uncertainties in habitat suitability maps , 2006 .

[54]  Xiwei Xu,et al.  Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China , 2012, Comput. Geosci..

[55]  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 .

[56]  J. Adinarayana,et al.  Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia , 2011, Environmental Monitoring and Assessment.

[57]  Christian Conoscenti,et al.  GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy) , 2008 .

[58]  S. G. Kim,et al.  Extreme Coefficients in Geographically Weighted Regression and Their Effects on Mapping , 2009 .

[59]  David Wheeler,et al.  Multicollinearity and correlation among local regression coefficients in geographically weighted regression , 2005, J. Geogr. Syst..

[60]  Biswajeet Pradhan,et al.  A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS , 2013, Comput. Geosci..

[61]  Vicki G. Moon,et al.  Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale , 2012 .

[62]  Daniel B. Carr,et al.  Hexagon Mosaic Maps for Display of Univariate and Bivariate Geographical Data , 1992 .