Comparison of statistical and machine learning approaches in land subsidence modelling

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).

Automated summary

This study explored the prediction of ground subsidence using various statistical and machine learning models in the Rafsanjan Plain in Iran, highlighting the importance of factors such as NDVI, groundwater drawdown, land use, and lithology. The SVM model showed the highest prediction accuracy among the tested models.