PMID- 35590797
OWN - NLM
STAT- MEDLINE
DCOM- 20220523
LR  - 20220523
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 9
DP  - 2022 Apr 19
TI  - Evaluation of Different Landslide Susceptibility Models for a Local Scale in the 
      Chitral District, Northern Pakistan.
LID - 10.3390/s22093107 [doi]
LID - 3107
AB  - This work evaluates the performance of three machine learning (ML) techniques, 
      namely logistic regression (LGR), linear regression (LR), and support vector 
      machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely 
      analytical hierarchy process (AHP) and the technique for order of preference by 
      similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in 
      the Chitral district, northern Pakistan. Moreover, we create landslide inventory 
      maps from LANDSAT-8 satellite images through the change vector analysis (CVA) 
      change detection method. The change detection yields more than 500 landslide 
      spots. After some manual post-processing correction, the landslide inventory 
      spots are randomly split into two sets with a 70/30 ratio for training and 
      validating the performance of the ML techniques. Sixteen topographical, 
      hydrological, and geological landslide-related factors of the study area are 
      prepared as GIS layers. They are used to produce landslide susceptibility maps 
      (LSMs) with weighted overlay techniques using different weights of 
      landslide-related factors. The accuracy assessment shows that the ML techniques 
      outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the 
      resulting LSM.
FAU - Aslam, Bilal
AU  - Aslam B
AUID- ORCID: 0000-0001-7308-5285
AD  - Department of Earth Sciences, Quaid-e-Azam University, Islamabad 45320, Pakistan.
FAU - Maqsoom, Ahsen
AU  - Maqsoom A
AUID- ORCID: 0000-0002-3745-516X
AD  - Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, 
      Pakistan.
FAU - Khalil, Umer
AU  - Khalil U
AUID- ORCID: 0000-0002-1095-3169
AD  - Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, 
      Pakistan.
FAU - Ghorbanzadeh, Omid
AU  - Ghorbanzadeh O
AUID- ORCID: 0000-0002-9664-8770
AD  - Institute of Advanced Research in Artificial Intelligence (IARAI), Landstrasser 
      Hauptstrasse 5, 1030 Vienna, Austria.
FAU - Blaschke, Thomas
AU  - Blaschke T
AUID- ORCID: 0000-0002-1860-8458
AD  - Department of Geoinformatics-Z_GIS, University of Salzburg, 5020 Salzburg, 
      Austria.
FAU - Farooq, Danish
AU  - Farooq D
AUID- ORCID: 0000-0003-3403-7744
AD  - Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, 
      Pakistan.
FAU - Tufail, Rana Faisal
AU  - Tufail RF
AD  - Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, 
      Pakistan.
FAU - Suhail, Salman Ali
AU  - Suhail SA
AUID- ORCID: 0000-0002-6384-9736
AD  - Department of Civil Engineering, University of Lahore (UOL), Lahore 54590, 
      Pakistan.
FAU - Ghamisi, Pedram
AU  - Ghamisi P
AUID- ORCID: 0000-0003-1203-741X
AD  - Institute of Advanced Research in Artificial Intelligence (IARAI), Landstrasser 
      Hauptstrasse 5, 1030 Vienna, Austria.
AD  - Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource 
      Technology, 09599 Freiberg, Germany.
LA  - eng
GR  - ATU74131236/Institute of Advanced Research in Artificial Intelligence (IARAI) 
      GmbH/
PT  - Journal Article
DEP - 20220419
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Geographic Information Systems
MH  - *Landslides
MH  - Logistic Models
MH  - Pakistan
MH  - Support Vector Machine
PMC - PMC9101762
OTO - NOTNLM
OT  - LANDSAT-8
OT  - analytical hierarchy process (AHP)
OT  - landslide susceptibility maps (LSMs)
OT  - linear regression (LR)
OT  - logistic regression (LGR)
OT  - machine learning (ML) techniques
OT  - support vector machines (SVM)
COIS- The authors declare no conflict of interest.
EDAT- 2022/05/21 06:00
MHDA- 2022/05/24 06:00
PMCR- 2022/04/19
CRDT- 2022/05/20 01:09
PHST- 2022/03/14 00:00 [received]
PHST- 2022/04/09 00:00 [revised]
PHST- 2022/04/15 00:00 [accepted]
PHST- 2022/05/20 01:09 [entrez]
PHST- 2022/05/21 06:00 [pubmed]
PHST- 2022/05/24 06:00 [medline]
PHST- 2022/04/19 00:00 [pmc-release]
AID - s22093107 [pii]
AID - sensors-22-03107 [pii]
AID - 10.3390/s22093107 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Apr 19;22(9):3107. doi: 10.3390/s22093107.