Research Article

Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction

Volume: 16 Number: 3 September 1, 2026
TR EN

Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction

Abstract

Soil temperature prediction is important for farming, climate research, and environmental modeling. This research proposes an ensemble prediction method for soil temperature prediction on a daily basis using lag feature and Gaussian noise. In the proposed framework, the ensemble algorithms Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting Regressor (GradientBoosting) are compared. The models are trained on a dataset enriched with lagged temperature values and synthetic noise and evaluated using metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R²) and Mean Absolute Percentage Error (MAPE). The results show that all models exhibited high prediction performance. However, the GradientBoosting model has the best performance with an R² value of approximately 0.96. To further assess the time series structure and model behavior in detail, diagnostic analyses such as autocorrelation analysis, box plots and residual histograms are also conducted. The proposed strategy works well for reliable short-term soil temperature prediction.

Keywords

Soil temperature prediction, Ensemble models, Gaussian noise augmentation, XGBoost, Gradient boosting, AdaBoost

Ethical Statement

The author declares that this study complies with Research and Publication Ethics.

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APA
Yıldırım, E., Mert, İ., & Özkan, A. (2026). Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction. Karadeniz Fen Bilimleri Dergisi, 16(3), 1156-1170. https://doi.org/10.31466/kfbd.1810051
AMA
1.Yıldırım E, Mert İ, Özkan A. Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction. KFBD. 2026;16(3):1156-1170. doi:10.31466/kfbd.1810051
Chicago
Yıldırım, Emre, İlker Mert, and Ali Özkan. 2026. “Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction”. Karadeniz Fen Bilimleri Dergisi 16 (3): 1156-70. https://doi.org/10.31466/kfbd.1810051.
EndNote
Yıldırım E, Mert İ, Özkan A (September 1, 2026) Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction. Karadeniz Fen Bilimleri Dergisi 16 3 1156–1170.
IEEE
[1]E. Yıldırım, İ. Mert, and A. Özkan, “Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction”, KFBD, vol. 16, no. 3, pp. 1156–1170, Sept. 2026, doi: 10.31466/kfbd.1810051.
ISNAD
Yıldırım, Emre - Mert, İlker - Özkan, Ali. “Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction”. Karadeniz Fen Bilimleri Dergisi 16/3 (September 1, 2026): 1156-1170. https://doi.org/10.31466/kfbd.1810051.
JAMA
1.Yıldırım E, Mert İ, Özkan A. Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction. KFBD. 2026;16:1156–1170.
MLA
Yıldırım, Emre, et al. “Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction”. Karadeniz Fen Bilimleri Dergisi, vol. 16, no. 3, Sept. 2026, pp. 1156-70, doi:10.31466/kfbd.1810051.
Vancouver
1.Emre Yıldırım, İlker Mert, Ali Özkan. Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction. KFBD. 2026 Sep. 1;16(3):1156-70. doi:10.31466/kfbd.1810051