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

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 12, 2025

Abstract 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 (XGBoost), a Meta Classifier (MC) were applied using Remote Sensing GIS tools identify key landslide-conditioning factors classify zones. Model performance was assessed through metrics such as accuracy, precision, recall, F1 score, AUC ROC curve. Among models, achieved highest accuracy (0.956) (0.987), demonstrating superior predictive ability. XGBoost, RF also performed well, accuracies 0.943 values 0.987 (GB XGBoost) 0.983 (RF). (ET) exhibited (0.946) among individual models 0.985. SVM LR, while slightly less accurate (0.941 0.860, respectively), provided valuable insights, achieving 0.972 LR 0.935. The effectively delineated into five zones (very low, moderate, high, very high), high concentrated Darjeeling Kalimpong subdivisions. These are influenced intense rainfall, unstable geological structures, anthropogenic activities like deforestation urbanization. Notably, ET, RF, GB, XGBoost demonstrated efficiency feature selection, requiring fewer input variables maintaining performance. establishes benchmark for mapping, providing scalable adaptable geospatial hazard prediction. findings hold implications land-use planning, disaster management, environmental conservation vulnerable regions worldwide.

Language: Английский

Mapping landslide susceptibility in the Eastern Mediterranean mountainous region: a machine learning perspective

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(9)

Published: April 30, 2025

Language: Английский