## 3 Methodology
### Model Selection and Classification Scheme  
ML models are widely used for various types of problems in natural hazard susceptibility mapping, including classification, clustering, and regression [2; 10; 70]. In the development of susceptibility maps for natural hazards using ML models, the data about previous incidents (inventory data) plays a crucial role in training and testing the model. Classification algorithms are particularly suitable for predicting discrete outcomes, such as the presence or absence of sinkholes, while clustering algorithms are used to group similar data points based on their features, and regression models are employed to predict continuous variables, such as the size or depth of sinkholes [71; 72]. In this study, we focused on classification tasks due to the presence of labeled data, where each sample was classified as either \"sinkhole\" or \"non-sinkhole\". Classification algorithms are specifically designed to handle labeled data and predict discrete class labels, making them suitable for our objective of mapping sinkhole susceptibility. In contrast, clustering algorithms are typically used for unsupervised learning tasks with unlabeled data, where the goal is to group similar data points based on their features. Since our dataset already contained predefined labels, we employed supervised classification algorithms to ensure high accuracy and interpretability in predicting sinkhole susceptibility. In the development of susceptibility maps for natural hazards using ML models, the data about previous incidents (inventory data) plays a crucial role in training and testing the model. In the training phase, the ML model learns from this inventory data to find patterns and understand how various factors contribute to the occurrence of natural hazards. Later, in the testing phase, the model's accuracy is checked using a new set of data that it has not seen before (not used during training). This helps the analyst see how well the model can predict natural hazard susceptibility in different situations [73; 74]. To make a thorough SSM, it is crucial to include both locations with sinkholes and those without. So, we carefully chose locations without sinkholes to match the number of locations with sinkholes in our data. This ensures an even coverage across the entire study area. We pay close attention to randomly selecting these non-sinkhole locations, which is essential for keeping a balance in the distribution of positive (sinkhole) and negative (non-sinkhole) samples in the dataset [16]. While no specific standard dictates the proportion of data allocated to training and testing subsets for ML models, a prevailing practice involves designating 70% of the inventory data for training and model refinement, with the remaining 30% reserved for testing and assessing model performance. In alignment with this convention, this study adheres to a similar split. Additionally, ensuring an equitable distribution of positive and negative samples in both the training and test datasets is crucial. Imbalances in class distribution can adversely impact the performance of classifier algorithms, underscoring the importance of maintaining symmetry in sample representation [11; 75]. In this study,567 sinkhole locations and an equal number of non-sinkhole locations were used for analysis in the GIS environment, with their respective labels as \"0\" and \"1\". Of the resulting 1194 samples, partitioned into 70% training and 30% test data, 396 samples are designated for testing (198 positive, 198 negative samples), while 798 samples serve as training data (399 positive, 399 negative samples). This research develops an SSM by leveraging ensemble algorithms, recognized for their efficacy in handling high-dimensional and intricate datasets. The ensemble algorithms employed in this study encompass Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The rationale for selecting ensemble algorithms in generating an SSM is threefold.