PMID- 33556831
OWN - NLM
STAT- MEDLINE
DCOM- 20210301
LR  - 20210301
IS  - 1095-8630 (Electronic)
IS  - 0301-4797 (Linking)
VI  - 284
DP  - 2021 Apr 15
TI  - Comparison of multi-criteria and artificial intelligence models for 
      land-subsidence susceptibility zonation.
PG  - 112067
LID - S0301-4797(21)00129-8 [pii]
LID - 10.1016/j.jenvman.2021.112067 [doi]
AB  - Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant 
      threat to sustainable land management. The purpose of this study is to predict 
      the LS distribution by generating land subsidence susceptibility models (LSSMs) 
      for the Shahroud plain in Iran using three different multi-criteria decision 
      making (MCDM) and five different artificial intelligence (AI) models. The MCDM 
      models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje 
      (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution 
      (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the 
      extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate 
      adaptive regression spline (BMARS) and conditional random forest (Cforest) 
      methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under 
      Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, 
      specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe 
      Criteria (NSC)to validate and evaluate the methods. Based on the different 
      validation techniques, the Cforest method yielded the best results with minimum 
      and maximum values of 0.04 and 0.99, respectively. According to the Cforest 
      model, 30.55% of the study area is extremely vulnerable to land subsidence. The 
      results of our research will be of great help to planners and policy makers in 
      the identification of the most vulnerable regions and the implementation of 
      appropriate development strategies in this area.
CI  - Copyright (c) 2021 Elsevier Ltd. All rights reserved.
FAU - Arabameri, Alireza
AU  - Arabameri A
AD  - Department of Geomorphology, Tarbiat Modares University, Tehran, 14117-13116, 
      Iran. Electronic address: a.arabameri@modares.ac.ir.
FAU - Chandra Pal, Subodh
AU  - Chandra Pal S
AD  - Department of Geography, The University of Burdwan, West Bengal, 713104, India. 
      Electronic address: scpal@geo.buruniv.ac.in.
FAU - Rezaie, Fatemeh
AU  - Rezaie F
AD  - Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral 
      Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon, 34132, Republic of Korea; 
      Korea University of Science and Technology, 217 Gajeong-roYuseong-gu, Daejeon, 
      34113, Republic of Korea.
FAU - Chakrabortty, Rabin
AU  - Chakrabortty R
AD  - Department of Geography, The University of Burdwan, West Bengal, 713104, India. 
      Electronic address: rabingeo8@gmail.com.
FAU - Chowdhuri, Indrajit
AU  - Chowdhuri I
AD  - Department of Geography, The University of Burdwan, West Bengal, 713104, India. 
      Electronic address: indrajitchowdhuri@gmail.com.
FAU - Blaschke, Thomas
AU  - Blaschke T
AD  - Department of Geoinformatics - Z_GIS, University of Salzburg, 5020, Salzburg, 
      Austria. Electronic address: thomas.blaschke@sbg.ac.at.
FAU - Thi Ngo, Phuong Thao
AU  - Thi Ngo PT
AD  - Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet 
      Nam. Electronic address: ngotphuongthao5@duytan.edu.vn.
LA  - eng
PT  - Journal Article
DEP - 20210205
PL  - England
TA  - J Environ Manage
JT  - Journal of environmental management
JID - 0401664
SB  - IM
MH  - *Artificial Intelligence
MH  - Bayes Theorem
MH  - Iran
MH  - ROC Curve
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Iran
OT  - Land subsidence
EDAT- 2021/02/09 06:00
MHDA- 2021/03/02 06:00
CRDT- 2021/02/08 20:18
PHST- 2020/11/17 00:00 [received]
PHST- 2021/01/06 00:00 [revised]
PHST- 2021/01/26 00:00 [accepted]
PHST- 2021/02/09 06:00 [pubmed]
PHST- 2021/03/02 06:00 [medline]
PHST- 2021/02/08 20:18 [entrez]
AID - S0301-4797(21)00129-8 [pii]
AID - 10.1016/j.jenvman.2021.112067 [doi]
PST - ppublish
SO  - J Environ Manage. 2021 Apr 15;284:112067. doi: 10.1016/j.jenvman.2021.112067. 
      Epub 2021 Feb 5.