A Novel Adaptive Ensemble Learning Approach for Driving Factor Identification in Land Degradation
Received: 11 May 2025 | Revised: 28 June 2025, 24 July 2025, 1 August 2025, and 6 August 2025 | Accepted: 9 August 2025 | Online: 8 December 2025
Corresponding author: Gangamma Hediyalad
Abstract
Land degradation is an urgent global concern, driven by complex interactions among environmental and anthropogenic factors. Identifying and understanding the optimal factors driving land degradation are essential for effective prediction and sustainable land management. This study introduces a novel Adaptive Ensemble Driving Factor Learning Model (AEDFLM) designed to identify and validate the most significant factors affecting land degradation. The model incorporates a Random Forest Feature Importance Module (RFFIM) for feature selection, a Driving Factor Bootstrap Sampler (DFBS) to ensure stability and robustness through random sampling, and a Driving Factor Expansion Module (DFEM) for scalable and efficient learning. Besides these core ensemble components, Support Vector Regressor (SVR) and Linear Regressor are employed to assess the influence of specific features, offering insights into both non-linear and linear interactions of less significant driving factors. The model's predictive accuracy was evaluated using Mean Squared Error (MSE) and R-squared (R²), while feature importance and permutation importance analyses validated the significance of the chosen factors. The model can be further expanded to include predictions for the Desertification Vulnerability Index (DVI), thereby enhancing its broader applicability in assessing land degradation risks across various geographical regions.
Keywords:
land degradation, driving factor, AEDFLM, RFFIM, SVRDownloads
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