Geo-environmental modeling of soil erosion risk: Insights from Random Forest and Gradient Boost Tree analysis in the Darjeeling Himalayan landscape

The Darjeeling Himalayan region, characterized by its complex topography and vulnerability to multiple environmental hazards, faces significant challenges including landslides, earthquakes, flash floods, and soil loss that critically threaten ecosystem stability. Among these challenges, soil erosion emerges as a silent disaster- a gradual yet relentless process whose impacts accumulate over time, progressively degrading landscape integrity and disrupting ecological sustainability. Unlike catastrophic events with immediate visibility, soil erosion's most devastating consequences often manifest decades later through diminished agricultural productivity, habitat fragmentation, and irreversible biodiversity loss. This study developed a scalable predictive framework employing Random Forest (RF) and Gradient Boosting Tree (GBT) machine learning models to assess and map soil erosion susceptibility across the region. A comprehensive geo-database was developed incorporating 11 erosion triggering factors: slope, elevation, rainfall, drainage density, topographic wetness index, normalized difference vegetation index, curvature, soil texture, land use, geology, and aspect. A total of 2,483 historical soil erosion locations were identified and randomly divided into two sets: 70% for model building and 30% for validation purposes. The models revealed distinct spatial patterns of erosion risks, with GBT classifying 60.50% of the area as very low susceptibility, while RF identified 28.92% in this category. Notable differences emerged in high-risk zone identification, with GBT highlighting 7.42% and RF indicating 2.21% as very high erosion susceptibility areas. Both models demonstrated robust predictive capabilities, with GBT achieving 80.77% accuracy and 0.975 AUC, slightly outperforming RF's 79.67% accuracy and 0.972 AUC. Analysis of predictor variables identified elevation, slope, rainfall and NDVI as the primary factors influencing erosion susceptibility, highlighting the complex interrelationship between geo-environmental factors and erosion processes. This research offers a strategic framework for targeted conservation and sustainable land management in the fragile Himalayan region, providing valuable insights to help policymakers implement effective soil erosion mitigation strategies and support long-term environmental sustainability.