The main aim of the present study was to produce and compare landslide susceptibility maps by using five machine learning techniques, namely, artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), random forest (RF) and, classification and regression tree (CART). The study area was determined as the Arhavi-Kabisre river basin, a region in which the most landslide incidents occur in Turkey. Firstly, a landslide inventory was produced by identifying a total of 252 landslides. Secondly, a total of 11 landslide conditioning factors were considered for the landslide susceptibility mapping. Subsequently, the five machine learning techniques were constructed with the help of the training dataset for the landslide susceptibility maps. Finally, the receiver operating characteristic (ROC), sensitivity, specificity, F-measure, accuracy and kappa index were applied to compare and validate the performance of the five machine learning techniques.

The present study aimed to compare five machine learning techniques, namely, artificial neural network (ANN), support vector machine (SVM), chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and random forest (RF) for mass appraisal of real estate. Firstly, 1982 precedent data was collected throughout the entire study area for train and test models. Secondly, a total of 68 variables were considered for the mass appraisal. Subsequently, the five machine learning techniques were applied. Finally, the receiver operating characteristic (ROC) and various statistical methods were applied to compare five machine learning techniques.

Sinkholes, naturally occurring formations in karst regions, represent a significant environmental hazard, threatening infrastructure, agricultural lands, and human safety. In recent years, machine learning (ML) techniques have been extensively employed for sinkhole susceptibility mapping (SSM). However, the lack of explainability inherent in these methods remains a critical issue for decision-makers. In this study, sinkhole susceptibility in the Konya Closed Basin was mapped using an interpretable machine learning model based on SHapley Additive exPlanations (SHAP). The Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were employed, and the interpretability of the model results was enhanced through SHAP analysis. Among the compared models, the RF model demonstrated the highest performance, achieving an accuracy of 95.5% and an AUC score of 98.8%, and was consequently selected for the development of the final susceptibility map. SHAP analyses revealed that factors such as proximity to fault lines, mean annual precipitation, and bicarbonate concentration difference are the most significant variables influencing sinkhole formation. Additionally, specific threshold values were quantified, and the critical effects of these contributing factors were analyzed in detail. This study underscores the importance of employing eXplainable Artificial Intelligence (XAI) techniques in natural hazard modeling, using SSM as an example, thereby providing decision-makers with a more reliable and comparable risk assessment.