PMID- 35590807
OWN - NLM
STAT- MEDLINE
DCOM- 20220523
LR  - 20220716
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 9
DP  - 2022 Apr 19
TI  - Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by 
      Persistent Scatterer In-SAR Technique.
LID - 10.3390/s22093119 [doi]
LID - 3119
AB  - Landslides are the most catastrophic geological hazard in hilly areas. The 
      present work intends to identify landslide susceptibility along Karakorum Highway 
      (KKH) in Northern Pakistan, using landslide susceptibility mapping (LSM). To 
      compare and predict the connection between causative factors and landslides, the 
      random forest (RF), extreme gradient boosting (XGBoost), k nearest neighbor (KNN) 
      and naive Bayes (NB) models were used in this research. Interferometric synthetic 
      aperture radar persistent scatterer interferometry (PS-InSAR) technology was used 
      to explore the displacement movement of retrieved models. Initially, 332 
      landslide areas alongside the Karakorum Highway were found to generate the 
      landslide inventory map using various data. The landslides were categorized into 
      two sections for validation and training, of 30% and 70%. For susceptibility 
      mapping, thirteen landslide-condition factors were created. The area under curve 
      (AUC) of the receiver operating characteristic (ROC) curve technique was utilized 
      for accuracy comparison, yielding 83.08, 82.15, 80.31, and 72.92% accuracy for 
      RF, XGBoost, KNN, and NB, respectively. The PS-InSAR technique demonstrated a 
      high deformation velocity along the line of sight (LOS) in model-sensitive areas. 
      The PS-InSAR technique was used to evaluate the slope deformation velocity, which 
      can be used to improve the LSM for the research region. The RF technique yielded 
      superior findings, integrating with the PS-InSAR outcomes to provide the region 
      with a new landslide susceptibility map. The enhanced model will help mitigate 
      landslide catastrophes, and the outcomes may help ensure the roadway's safe 
      functioning in the study region.
FAU - Hussain, Muhammad Afaq
AU  - Hussain MA
AD  - School of Geography and Information Engineering, China University of Geosciences 
      (Wuhan), Wuhan 430074, China.
FAU - Chen, Zhanlong
AU  - Chen Z
AUID- ORCID: 0000-0001-6373-3162
AD  - School of Geography and Information Engineering, China University of Geosciences 
      (Wuhan), Wuhan 430074, China.
FAU - Zheng, Ying
AU  - Zheng Y
AD  - School of Geography and Information Engineering, China University of Geosciences 
      (Wuhan), Wuhan 430074, China.
FAU - Shoaib, Muhammad
AU  - Shoaib M
AD  - State Key Laboratory of Hydraulic Engineering, Simulation and Safety, School of 
      Civil Engineering, Tianjin University, Tianjin 300072, China.
FAU - Shah, Safeer Ullah
AU  - Shah SU
AD  - Ministry of Climate Change, Islamabad 44000, Pakistan.
FAU - Ali, Nafees
AU  - Ali N
AD  - Chinese Academy of Sciences, Beijing 100045, China.
FAU - Afzal, Zeeshan
AU  - Afzal Z
AD  - Key State Laboratory of Information Engineering in Surveying, Mapping and Remote 
      Sensing (LIESMARS), Wuhan University, Wuhan 430079, China.
LA  - eng
GR  - No. 41871305/National Natural Science Foundation of China/
GR  - No.2017YFC0602204/National key R & D program of China/
GR  - No. CUGQY1945/Fundamental Research Funds for the Central Universities, China 
      University of Geosciences (Wuhan)/
GR  - No. GLAB2019ZR02/Opening Fund of Key Laboratory of Geological Survey and 
      Evaluation of Ministry of Education; and Fundamental Research Funds for the 
      Central Universities/
PT  - Journal Article
DEP - 20220419
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - Bayes Theorem
MH  - Geographic Information Systems
MH  - *Landslides
MH  - Machine Learning
PMC - PMC9102666
OTO - NOTNLM
OT  - ArcGIS
OT  - CPEC
OT  - PS-InSAR
OT  - landslides
OT  - random forest
OT  - susceptibility
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- 2022/03/11 00:00 [received]
PHST- 2022/03/31 00:00 [revised]
PHST- 2022/04/17 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 - s22093119 [pii]
AID - sensors-22-03119 [pii]
AID - 10.3390/s22093119 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Apr 19;22(9):3119. doi: 10.3390/s22093119.