PMID- 27019609
OWN - NLM
STAT- Publisher
LR  - 20191120
IS  - 1365-8816 (Print)
IS  - 1365-8816 (Linking)
VI  - 28
IP  - 3
DP  - 2014 Mar 4
TI  - An uncertainty and sensitivity analysis approach for GIS-based multicriteria 
      landslide susceptibility mapping.
PG  - 610-638
AB  - GIS-based multicriteria decision analysis (MCDA) methods are increasingly being 
      used in landslide susceptibility mapping. However, the uncertainties that are 
      associated with MCDA techniques may significantly impact the results. This may 
      sometimes lead to inaccurate outcomes and undesirable consequences. This article 
      introduces a new GIS-based MCDA approach. We illustrate the consequences of 
      applying different MCDA methods within a decision-making process through 
      uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo 
      simulation (MCS) and Dempster-Shafer theory are analyzed for landslide 
      susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly 
      susceptible to landslide hazards. The methodology comprises three stages. First, 
      the LSM criteria are ranked and a sensitivity analysis is implemented to simulate 
      error propagation based on the MCS. The resulting weights are expressed through 
      probability density functions. Accordingly, within the second stage, three MCDA 
      methods, namely analytical hierarchy process (AHP), weighted linear combination 
      (WLC) and ordered weighted average (OWA), are used to produce the landslide 
      susceptibility maps. In the third stage, accuracy assessments are carried out and 
      the uncertainties of the different results are measured. We compare the 
      accuracies of the three MCDA methods based on (1) the Dempster-Shafer theory and 
      (2) a validation of the results using an inventory of known landslides and their 
      respective coverage based on object-based image analysis of IRS-ID satellite 
      images. The results of this study reveal that through the integration of GIS and 
      MCDA models, it is possible to identify strategies for choosing an appropriate 
      method for LSM. Furthermore, our findings indicate that the integration of MCDA 
      and MCS can significantly improve the accuracy of the results. In LSM, the AHP 
      method performed best, while the OWA reveals better performance in the 
      reliability assessment. The WLC operation yielded poor results.
FAU - Feizizadeh, Bakhtiar
AU  - Feizizadeh B
AD  - Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria; 
      Center for Remote Sensing and GIS, University of Tabriz, Tabriz, Iran.
FAU - Blaschke, Thomas
AU  - Blaschke T
AD  - Department of Geoinformatics - Z_GIS, University of Salzburg , Salzburg , 
      Austria.
LA  - eng
PT  - Journal Article
DEP - 20140120
PL  - England
TA  - Int J Geogr Inf Sci
JT  - International journal of geographical information science : IJGIS
JID - 101086096
PMC - PMC4786847
OTO - NOTNLM
OT  - Dempster-Shafer Theory
OT  - GIS-MCDA
OT  - Monte Carlo simulation
OT  - Urmia lake basin
OT  - landslide susceptibility mapping
OT  - sensitivity analysis
EDAT- 2014/03/04 00:00
MHDA- 2014/03/04 00:00
PMCR- 2016/03/11
CRDT- 2016/03/29 06:00
PHST- 2013/07/07 00:00 [received]
PHST- 2013/11/19 00:00 [accepted]
PHST- 2016/03/29 06:00 [entrez]
PHST- 2014/03/04 00:00 [pubmed]
PHST- 2014/03/04 00:00 [medline]
PHST- 2016/03/11 00:00 [pmc-release]
AID - 869821 [pii]
AID - 10.1080/13658816.2013.869821 [doi]
PST - ppublish
SO  - Int J Geogr Inf Sci. 2014 Mar 4;28(3):610-638. doi: 10.1080/13658816.2013.869821. 
      Epub 2014 Jan 20.