Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting

Reşat Acar; Okan Mert Katipoğlu; Burak Çırağ

doi:10.64808/engineeringperspective.1906862

Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting

Abstract

Getting accurate daily estimates of reference evapotranspiration (ET₀) is essential for hydrological modeling, irrigation planning, and climate research. This study evaluated the performance of four machine learning models: Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Deep Neural Network (DNN), and Light Gradient Boosting Machine (LGBM) for daily ET₀ prediction. Feature selection was implemented using Partial Autocorrelation (PACF) and Autocorrelation (ACF) analysis on the basis of an 8-day lag structure, while the CatBoost feature importance framework was implemented to identify the most important inputs. Moreover, the methodologies of decomposition-based preprocessing, Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) were implemented on the lagged inputs to build the hybrid models augmented with intrinsic constituents. These augmented hybrid models were created to improve the data representation as well as predictive capability. Model performance was evaluated using key statistical metrics, including RMSE, MAE, NSE, KGE, R2, and bias-related indicators. CatBoost produced the lowest RMSE (1.57 mm/day) and MAE (1.13 mm/day), and the highest NSE (0.67), KGE (0.78), and R2 (0.67), along with the lowest AIC score (627.15), indicating strong and reliable predictive performance. Furthermore, the VMD based CatBoost model yielded even greater improvements, highlighting the added value of decomposition enhanced learning. On the contrary, DNN showed the weakest results, with the highest RMSE (2.39 mm/day) and lowest NSE (0.23). These findings suggest that CatBoost and its hybrid variants are highly effective and reliable tools for daily ET₀ prediction, particularly in data limited environments and mountainous regions.

Keywords

References

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Details

Primary Language

English

Subjects

Water Resources Engineering

Journal Section

Research Article

Authors

Reşat Acar
0000-0002-0653-1991
Türkiye

Okan Mert Katipoğlu
0000-0001-6421-6087
Türkiye

Burak Çırağ ^*
0000-0002-7721-5128
Türkiye

Publication Date

May 13, 2026

Submission Date

March 10, 2026

Acceptance Date

May 11, 2026

Published in Issue

Year 2026 Volume: 6 Number: 3

DOI

https://doi.org/10.64808/engineeringperspective.1906862

IZ

https://izlik.org/JA66AD48CM

Cite

RIS / Bibtex

APA

Acar, R., Katipoğlu, O. M., & Çırağ, B. (2026). Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting. Engineering Perspective, 6(3), 354-373. https://doi.org/10.64808/engineeringperspective.1906862

AMA

1.Acar R, Katipoğlu OM, Çırağ B. Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting. engineeringperspective. 2026;6(3):354-373. doi:10.64808/engineeringperspective.1906862

Chicago

Acar, Reşat, Okan Mert Katipoğlu, and Burak Çırağ. 2026. “Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting”. Engineering Perspective 6 (3): 354-73. https://doi.org/10.64808/engineeringperspective.1906862.

EndNote

Acar R, Katipoğlu OM, Çırağ B (May 1, 2026) Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting. Engineering Perspective 6 3 354–373.

IEEE

[1]R. Acar, O. M. Katipoğlu, and B. Çırağ, “Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting”, engineeringperspective, vol. 6, no. 3, pp. 354–373, May 2026, doi: 10.64808/engineeringperspective.1906862.

ISNAD

Acar, Reşat - Katipoğlu, Okan Mert - Çırağ, Burak. “Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting”. Engineering Perspective 6/3 (May 1, 2026): 354-373. https://doi.org/10.64808/engineeringperspective.1906862.

JAMA

1.Acar R, Katipoğlu OM, Çırağ B. Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting. engineeringperspective. 2026;6:354–373.

MLA

Acar, Reşat, et al. “Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting”. Engineering Perspective, vol. 6, no. 3, May 2026, pp. 354-73, doi:10.64808/engineeringperspective.1906862.

Vancouver

1.Reşat Acar, Okan Mert Katipoğlu, Burak Çırağ. Hybrid VMD-EMD Enhanced Machine Learning Models for Daily Reference Evapotranspiration Forecasting. engineeringperspective. 2026 May 1;6(3):354-73. doi:10.64808/engineeringperspective.1906862