Landslide susceptibility assessment in the hilly valleys of the Pays d'Auge using logistic regression (Normandie, France)

In Europe, many studies about landslides have been performed in mountainous environments. However, a large proportion of sloping hilly valleys in Western Europe are also affected by slope instabilities. This paper presents a first attempt of landslide susceptibility mapping on a selected representative area of 24 km² located in the Pays d'Auge plateau in Normandy (France). The main objective is to define a quick reproductive indirect mapping technique at 1:10.000 scale with a set of rapid available data. The technique could be used as an operational mapping technique. In this case, only shallow landslide susceptibility was assessed by the logistic regression technique. The conclusions show interesting results in terms of high susceptibility areas locations, nevertheless, the model performances can still be improved by the introduction of new dataset.

[1]  R. Soeters,et al.  Landslide hazard and risk zonation—why is it still so difficult? , 2006 .

[2]  Hervas De Diego Francisco,et al.  Landslide Susceptibility Mapping at 1:1 M Scale over France: Exploratory Results with a Heuristic Model , 2009 .

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

[4]  L. Luzi,et al.  The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods , 2002 .

[5]  Eric R. Ziegel,et al.  Logistic Regression Using the SAS® System@@@Logistic Regression Using the SAS registered System , 2000 .

[6]  G. Grandjean,et al.  Landslide susceptibility mapping at 1:1M scale over France , 2009 .

[7]  M. Eeckhaut,et al.  Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium) , 2006 .

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

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

[10]  D. Varnes,et al.  Landslide types and processes , 2004 .

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

[12]  V. Jetten,et al.  Quantitative landslide hazard assessment along a transportation corridor in southern India , 2010 .

[13]  C. J. Westen.  The Modelling Of Landslide Hazards Using Gis , 2000 .

[14]  Andrew E. G. Jonas.  A Review of “Scale” , 2012 .

[15]  Paul D. Allison,et al.  Logistic Regression Using the SAS System : Theory and Application , 1999 .

[16]  R. Soeters,et al.  Slope instability recognition, analysis, and zonation , 1996 .

[17]  P. Reichenbach,et al.  Gis Technology in Mapping Landslide Hazard , 1995 .

[18]  I. Gibson.  Statistics and Data Analysis in Geology , 1976, Mineralogical Magazine.

[19]  Simone Sterlacchini,et al.  Strategy to Reduce Subjectivity in Landslide Susceptibility Zonation by GIS in Complex Mountainous Environments , 2004 .

[20]  M. Eeckhaut,et al.  Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France). , 2010 .

[21]  C. Lienhard.  Inventaire préliminaire des Psoques de Grèce (Insecta: Psocoptera) , 1987 .

[22]  T. Glade,et al.  A Review of Scale Dependency in Landslide Hazard and Risk Analysis , 2012 .

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

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