Spatial prediction models for landslide hazards: review, comparison and evaluation

Abstract. The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting "present" and "future" landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.

[1]  B. Efron,et al.  A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .

[2]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[3]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[4]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[5]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[6]  J. Booth,et al.  Resampling-Based Multiple Testing. , 1994 .

[7]  S. T. Buckland,et al.  An autologistic model for the spatial distribution of wildlife , 1996 .

[8]  Richard Dikau,et al.  Landslide recognition : identification, movement and courses , 1996 .

[9]  W. W. Stroup,et al.  A Generalized Linear Model Approach to Spatial Data Analysis and Prediction , 1997 .

[10]  David J. Hand,et al.  Construction and Assessment of Classification Rules , 1997 .

[11]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[12]  P. Atkinson,et al.  Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy , 1998 .

[13]  Mark R. Wade,et al.  Construction and Assessment of Classification Rules , 1999, Technometrics.

[14]  J. L. Hubp.  Landslide recognition: identification, movement and causes , 1999 .

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[16]  Erwin Beck,et al.  Analysis of undisturbed and disturbed tropical mountain forest ecosystems in Southern Ecuador , 2001 .

[17]  Chang-Jo Chung,et al.  Fuzzy Logic Integration for Landslide Hazard Mapping Using Spatial Data from Boeun, Korea , 2022 .

[18]  Fausto Guzzetti,et al.  Impact of mapping errors on the reliability of landslide hazard maps , 2002 .

[19]  DAMOCLES: Debrisfall Assessment in Mountain Catchments for Local End-users , 2002 .

[20]  S. Beguería,et al.  DAMOCLES DEBRISFALL ASSESSMENT IN MOUNTAIN CATCHMENTS FOR LOCAL END-USERS , 2002 .

[21]  M. Richter.  Using epiphytes and soil temperatures for eco-climatic interpretations in southern Ecuador , 2003 .

[22]  Saro Lee,et al.  Landslide susceptibility analysis using GIS and artificial neural network , 2003 .

[23]  USE OF QUANTITATIVE TECHNIQUES FOR ZONING LANDSLIDE HAZARD , 2003 .

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

[25]  J. Corominas,et al.  A GIS-Based Multivariate Statistical Analysis for Shallow Landslide Susceptibility Mapping in La Pobla de Lillet Area (Eastern Pyrenees, Spain) , 2003 .

[26]  R. Soeters,et al.  Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment , 2003 .

[27]  Walter Krämer,et al.  Review of Modern applied statistics with S, 4th ed. by W.N. Venables and B.D. Ripley. Springer-Verlag 2002 , 2003 .

[28]  Torsten Hothorn,et al.  Double-Bagging: Combining Classifiers by Bootstrap Aggregation , 2002, Pattern Recognit..

[29]  W. Dietrich,et al.  Testing a model for predicting the timing and location of shallow landslide initiation in soil‐mantled landscapes , 2003 .

[30]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

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

[32]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[33]  Archontoula Chaloulakou,et al.  Classification criteria and probability risk maps: limitations and perspectives. , 2004, Environmental science & technology.

[34]  Alexander Brenning,et al.  Climatic and geomorphological controls of rock glaciers in the Andes of Central Chile , 2005 .

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

[36]  M. Anderson,et al.  Landslide hazard and risk , 2005 .