Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework

• Published in: Scientific reports Vol. 15; no. 1; pp. 5170 - 20
• Main Authors: Halder, Krishnagopal, Srivastava, Amit Kumar, Ghosh, Anitabha, Das, Subhabrata, Banerjee, Santanu, Pal, Subodh Chandra, Chatterjee, Uday, Bisai, Dipak, Ewert, Frank, Gaiser, Thomas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.02.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 704/172/4081; 704/4111; Accuracy; Deforestation; Disaster management; Emergency preparedness; Geological structures; Humanities and Social Sciences; Land use; Land use planning; Landslide; Landslides; Landslides & mudslides; Learning algorithms; Machine learning; multidisciplinary; Predictions; Recursive feature elimination, meta-learning framework; Regression analysis; Remote sensing; Science; Science (multidisciplinary); Support vector machines; Susceptibility; Urbanization; Recursive feature elimination, meta-learning framework; Landslide; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-87587-3

Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble framework integrating Recursive Feature Elimination (RFE) with meta-learning techniques. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and a Meta Classifier (MC) were applied using Remote Sensing and GIS tools to identify key landslide-conditioning factors and classify susceptibility zones. Model performance was assessed through metrics such as accuracy, precision, recall, F1 score, and AUC of the ROC curve. Among the models, the Meta Classifier (MC) achieved the highest accuracy (0.956) and AUC (0.987), demonstrating superior predictive ability. Gradient Boosting (GB), XGBoost, and RF also performed well, with accuracies of 0.943 and AUC values of 0.987 (GB and XGBoost) and 0.983 (RF). Extremely Randomized Trees (ET) exhibited the highest accuracy (0.946) among individual models and an AUC of 0.985. SVM and LR, while slightly less accurate (0.941 and 0.860, respectively), provided valuable insights, with SVM achieving an AUC of 0.972 and LR achieving 0.935. The models effectively delineated landslide susceptibility into five zones (very low, low, moderate, high, and very high), with high and very high susceptibility zones concentrated in Darjeeling and Kalimpong subdivisions. These zones are influenced by intense rainfall, unstable geological structures, and anthropogenic activities like deforestation and urbanization. Notably, ET, RF, GB, and XGBoost demonstrated efficiency in feature selection, requiring fewer input variables while maintaining high performance. This study establishes a benchmark for landslide susceptibility mapping, providing a scalable and adaptable framework for geospatial hazard prediction. The findings hold significant implications for land-use planning, disaster management, and environmental conservation in vulnerable regions worldwide.