A Data-Driven Approach to Landslide-Susceptibility Mapping in Mountainous Terrain: Case Study from the Northwest Himalayas, Pakistan

2018 ◽  
Vol 19 (4) ◽  
pp. 05018007 ◽  
Author(s):  
Muhammad Tayyib Riaz ◽  
Muhammad Basharat ◽  
Nasir Hameed ◽  
Muhammad Shafique ◽  
Jin Luo
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Data Driven ◽  
Landslide Susceptibility Mapping ◽  
Mountainous Terrain ◽  
Data Driven Approach ◽  
Northwest Himalayas

scholarly journals Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France)

2013 ◽  
Vol 1 (2) ◽  
pp. 957-1000 ◽  
Author(s):  
M. Fressard ◽  
Y. Thiery ◽  
O. Maquaire
Keyword(s):  
Landslide Susceptibility ◽  
Roc Curves ◽  
Statistical Modelling ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Statistical Accuracy ◽  
Multivariate Statistical ◽  
The Impact ◽  
Modelling Methods

Abstract. The objective of this paper is to assess the impact of the datasets quality for the landslide susceptibility mapping using multivariate statistical modelling methods at detailed scale. This research is conducted in the Pays d'Auge plateau (Normandy, France) with a scale objective of 1/10000, in order to fit the French guidelines on risk assessment. Five sets of data of increasing quality (considering accuracy, scale fitting, geomophological significance) and cost of acquisition are used to map the landslide susceptibility using logistic regression. The best maps obtained with each set of data are compared on the basis of different statistical accuracy indicators (ROC curves and relative error calculation), linear cross correlation and expert opinion. The results highlights that only high quality sets of data supplied with detailed geomorphological variables (i.e. field inventory and surficial formations maps) can predict a satisfying proportion of landslides on the study area.

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