Land Subsidence Spatial Modeling and Assessment of the Contribution of Geo-Environmental Factors to Land Subsidence: Comparison of Different Novel Ensemble Modeling Approaches
Abstract Land subsidence is a worldwide threat. In arid and semiarid land, groundwater depletion is the main factor that induce the subsidence and results in environmental damages, with high economic losses. To foresee and prevent the impact of land subsidence is necessary to develop accurated maps of the magnitude and evolution of the subsidences. Land subsidence susceptibility maps (LSSMs) provide one of the effective tools to manage vulnerable areas, and to reduce or prevent land subsidence. In this study, we used a new approach to improve Decision Stump Classification (DSC) performance and combine it with machine learning algorithms (MLAs) of Naive Bayes Tree (NBTree), J48 decision tree, alternating decision tree (ADTree), logistic model tree (LMT) and support vector machine (SVM) in land subsidence susceptibility mapping (LSSSM). We employ data from 94 subsidence locations, among which 70% were used to train learning hybrid models, and the other 30% were used for validation. In addition, the models’ performance was assessed by ROC-AUC, accuracy, sensitivity, specificity, odd ratio, root-mean-square error (RMSE), Kappa, frequency ratio and F-score techniques. A comparison of the results obtained from the different models, reveal that the new DSC-ADTree hybrid algorithm has the highest accuracy (AUC = 0.983) in preparing LSSSMs as compared to other learning models such as DSC-J48 (AUC = 0.976), DSC-NBTree (AUC = 0.959), DSC-LMT (AUC = 0.948), DSC-SVM (AUC = 0.939) and DSC (AUC = 0.911). The LSSSMs generated through the novel scientific approach presented in our study provide reliable tools for managing and reducing the risk of land subsidence.