Comparison of statistical and machine learning approaches in land subsidence modelling. Issue 21 (2nd November 2022)

• Record Type: Journal Article
• Authors: Rafiei Sardooi, Elham; Pourghasemi, Hamid Reza; Azareh, Ali; Soleimani Sardoo, Farshad; Clague, John J.
• Is Part Of: Geocarto international. Volume 37:Issue 21(2023)
• Page Start: 6165
• Page End: 6185
• Publication Date: 2022-11-02
• Subjects: Statistical models -- machine learning -- Boruta algorithm -- land subsidence prediction; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1933211
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC = 0.967, TSS = 0.91), followed by RF (AUC = 0.936, TSS = 0.87), EBF (AUC = 0.907, TSS = 0.83), and IoE (AUC= 0.88, TSS = 0.8).