干旱区生态环境知识资源中心(Arid): Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation

Arid

DOI	10.1016/j.jenvman.2021.112067
	Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation
	Arabameri, Alireza; Pal, Subodh Chandra; Rezaie, Fatemeh; Chakrabortty, Rabin; Chowdhuri, Indrajit; Blaschke, Thomas; Ngo, Phuong Thao Thi
通讯作者	Arabameri, A (corresponding author), Tarbiat Modares Univ, Dept Geomorphol, Tehran 1411713116, Iran.
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2021
卷号	284
英文摘要	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.
英文关键词	Land subsidence Artificial intelligence Iran
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000621653800001
WOS关键词	FUZZY INFERENCE SYSTEM ; EVIDENTIAL BELIEF FUNCTION ; COMMITTEE MACHINE ; RANDOM FOREST ; LOGISTIC-REGRESSION ; SEMIARID REGIONS ; NEURAL-NETWORK ; SURFACE-WATER ; GROUNDWATER ; ENSEMBLE
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350790
作者单位	[Arabameri, Alireza] Tarbiat Modares Univ, Dept Geomorphol, Tehran 1411713116, Iran; [Pal, Subodh Chandra; Chakrabortty, Rabin; Chowdhuri, Indrajit] Univ Burdwan, Dept Geog, Burdwan 713104, W Bengal, India; [Rezaie, Fatemeh] Korea Inst Geosci & Mineral Resources, Geosci Platform Res Div, 124 Gwahak Ro, Daejeon 34132, South Korea; [Rezaie, Fatemeh] Korea Univ Sci & Technol, 217 Gajeong Ro, Daejeon 34113, South Korea; [Blaschke, Thomas] Univ Salzburg, Dept Geoinformat GIS, A-5020 Salzburg, Austria; [Ngo, Phuong Thao Thi] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Arabameri, Alireza,Pal, Subodh Chandra,Rezaie, Fatemeh,et al. Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation[J],2021,284.
APA	Arabameri, Alireza.,Pal, Subodh Chandra.,Rezaie, Fatemeh.,Chakrabortty, Rabin.,Chowdhuri, Indrajit.,...&Ngo, Phuong Thao Thi.(2021).Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation.JOURNAL OF ENVIRONMENTAL MANAGEMENT,284.
MLA	Arabameri, Alireza,et al."Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation".JOURNAL OF ENVIRONMENTAL MANAGEMENT 284(2021).