## 1 Introduction  
Coal mining was once the driving force of the national industry and economic development in Korea, but this situation changed as demand for coal decreased. Gangwon Province was once Korea's largest coal mining area but most of its mines were closed in the early 1990s. Among the environmental problems that follow mine closures, land subsidence events can threaten human life and damage property and infrastructure, including buildings, houses, railroads, and roads [1; 2; 3; 4]. Recovery of surface structures following land subsidence is difficult and costly; therefore, it is necessary to predict land subsidence susceptibility (LSS) zones before subsidence occurs, and to implement management strategies in these zones [3].  
Generally, prediction of subsidence susceptibility zones requires the input of several environmental factors and the application of perdition models [5]. Several previous studies have developed quantitative and qualitative models that have been successfully applied in various hazard susceptibility zones worldwide [3; 4; 5; 6; 7; 8; 9; 10; 11]. These include logistic regression (LR) [3], frequency ratio (FR) [3; 6], weight of evidence (WOE) [3], evidential belief function (EBF) [4], artificial neural network (ANN) [3; 5; 7; 8], support vector machine (SVM) [9], random forest (RF) [10], and fuzzy logic (FL) [8; 11] models. Single LSS mapping models can be combined to form ensemble models, which provide more precise and meaningful results [9]. Ensemble models based on machine learning have recently improved the prediction accuracy and performance of single classifiers [12]. The main advantages of this approach are the ability to represent complex relationships between influential factors, and to incorporate spatial data of various scales [13].  
Based on existing studies, probability and statistical models using geographical information systems (GIS) have been applied extensively to predict the susceptibly of geohazards, such as landslides, floods, subsidence, and rockfalls [3; 14; 15; 16]. Recently, data mining and machine learning models for addressing nonlinear problems have been developed, which have been applied frequently and had their performances compared in landslide susceptibility mapping [17; 18; 19; 20]. In ground subsidence hazard mapping, ground subsidence hazard maps around abandoned underground coal mines (AUCMs) have been constructed by integrating the adaptive neuro-fuzzy inference system and GIS [21]. In addition, a fuzzy operator, decision tree with the CHAID and QUEST algorithms, and the frequency ratio have been applied to construct subsidence susceptibility maps at AUCMs in Korea [2; 11]. In this study, we investigated the performance of some models that have never been applied to land subsidence prediction. Therefore, in this study, we generated LSS maps for a South Korean district containing abandoned subsurface coal mines using machine learning methods, including a logit boost meta-ensemble model, two Bayesian models (Bayes net and NB models) and two functional models (logistic and multilayer perceptron models). The reliability and accuracy of all models were assessed by comparing their area under the receiver operating characteristic (ROC) curves. Data processing was performed using WEKA 3.9.2 and ArcGIS 10.5 software to produce five machine learning algorithms.