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

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) wi...