A stacked ensemble machine learning technique for landslide susceptibility mapping in Meghalaya, India

Landslides are devastating natural disasters, resulting in loss of life and property. We can reduce the impact of landslides by identifying the susceptible areas and regularly monitoring slope instability. This article presents a novel stacked ensemble machine learning (ML) model for landslide susceptibility mapping in Meghalaya, India. The proposed stacking model consists of the base layer and the meta layer. The base layer employed Logistic Regression, XGBoost, Random Forest, LightGBM, Support Vector Classifier, Extra Trees, k-Nearest Neighbours, Decision Tree, Gaussian Naive Bayes, and Quadratic Discriminant Analysis as the base learners. A neural network meta learner then processes the intermediate features (meta-features) to predict the probability of landslide occurrence. Recursive feature elimination with cross validation and the variance inflation factor were employed to determine the optimum features. Sixteen landslide causative factors, along with 2070 landslide and non-landslide points, were utilised to train and evaluate the proposed model. The proposed model's performance was assessed using accuracy, AUC, F1-score, precision, recall, and Kappa. It outperformed the individual base learners and advanced ML methods, including deep learning and hybrid approaches. Finally, the proposed model was employed for generating a landslide susceptibility map of Meghalaya.