<![CDATA[Fuel consumption prediction models deployed in mining operations often degrade in performance due to changes in the distribution of high-frequency telemetry data, a phenomenon commonly associated with concept drift. Static machine learning models trained on historical data may therefore lose reliability over time in dynamic operational environments. This study aims to develop an adaptive regression approach for predicting fuel consumption in mining haul trucks by integrating a Gradient Boosting Regressor with batch-wise performance monitoring and periodic retraining. Real-world telematics data were processed through systematic preprocessing and feature engineering to derive behavioral and operational indicators relevant to fuel usage. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²), while drift monitoring employed a threshold-based MAE analysis over streaming batches. Experimental results show that the initial model achieved an MAE of 27.27 L/h and an R² of 0.759, and the adaptive retraining strategy provided marginal yet consistent performance stabilization without detecting significant drift within the observed period. Beyond the mining application, this framework contributes to the development of lightweight adaptive regression systems for real-time data stream processing, supporting computationally efficient predictive maintenance in industrial IoT environments.]]>
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