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Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction

Cilt: 16 Sayı: 3 1 Eylül 2026
Emre Yıldırım *, İlker Mert , Ali Özkan
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Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction

Öz

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.

Anahtar Kelimeler

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

Etik Beyan

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

Kaynakça

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  5. Feng, Y., Cui, N., Hao, W., Gao, L., & Gong, D. (2019). Estimation of soil temperature from meteorological data using different machine learning models. Geoderma, 338, 67-77.
  6. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. https://doi.org/10.1006/jcss.1997.1504
  7. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
  8. Guleryuz, D. (2022). Estimation of soil temperatures with machine learning algorithms—Giresun and Bayburt stations in Turkey. Theoretical and Applied Climatology, 147(1), 109-125.
  9. Imanian, H., Mohammadian, A., Farhangmehr, V., Payeur, P., Goodarzi, D., Hiedra Cobo, J., & Shirkhani, H. (2024). A comparative analysis of deep learning models for soil temperature prediction in cold climates. Theoretical and Applied Climatology, 155(4), 2571-2587.
  10. Khanesar, M. A., Kayacan, E., Teshnehlab, M., & Kaynak, O. (2011). Analysis of the noise reduction property of type-2 fuzzy logic systems using a novel type-2 membership function. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(5), 1395-1406.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Enerji

Bölüm

Araştırma Makalesi

Yazarlar

Yayımlanma Tarihi

1 Eylül 2026

Gönderilme Tarihi

24 Ekim 2025

Kabul Tarihi

2 Temmuz 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 16 Sayı: 3

DOI

https://doi.org/10.31466/kfbd.1810051

IZ

https://izlik.org/JA68NH66RS

Kaynak Göster

RIS / Bibtex
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, ve 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 (01 Eylül 2026) Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction. Karadeniz Fen Bilimleri Dergisi 16 3 1156–1170.
IEEE
[1]E. Yıldırım, İ. Mert, ve A. Özkan, “Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction”, KFBD, c. 16, sy 3, ss. 1156–1170, Eyl. 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 (01 Eylül 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, vd. “Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction”. Karadeniz Fen Bilimleri Dergisi, c. 16, sy 3, Eylül 2026, ss. 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. 01 Eylül 2026;16(3):1156-70. doi:10.31466/kfbd.1810051


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