Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation. (15th April 2021)

• Record Type: Journal Article
• Authors: Arabameri, Alireza; Chandra Pal, Subodh; Rezaie, Fatemeh; Chakrabortty, Rabin; Chowdhuri, Indrajit; Blaschke, Thomas; Thi Ngo, Phuong Thao
• Is Part Of: Journal of environmental management. Volume 284(2021)
• Publication Date: 2021-04-15
• Subjects: Land subsidence -- Artificial intelligence -- Iran; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2021.112067
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: 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. Highlights: LS susceptibility maps were derived from various models of MCDM and AI approaches. The AI based Cforest model provided highest prediction accuracy of 99.2% ROC-AUC. All maps were depicted that 6–27% of areas have very high prone to subsidence. LULC and groundwater are the most determinant factor of LS in the Shahroud plain.