Abstract
Accurate estimation of reference evapotranspiration (ET0) is a crucial parameter in implementing precise irrigation strategies and managing regional water resources effectively. While various methods have been proposed to obtain accurate ET0, the conventional approach is complex, uneconomical, and unable to contend with the rising variability and unpredictable weather patterns. Meanwhile, the lack of meteorological data limits the accurate estimation of ET0 via empirical models. Considering the recent approach in coupling ML techniques with optimisation algorithms to enhance the accuracy and robustness of ET0 estimation, this study was conducted to explore the performance of optimised hybrid Support Vector Regression (SVR) models integrated with meta-heuristic algorithms for daily ET0 estimation in Malaysia. Four hybrid SVR models, including SVR-Particle Swarm Optimisation (SVR-PSO), SVR-Whale Optimisation Algorithm (SVR-WOA), SVR-Differential Evolution (SVR-DE), and SVR-Covariance Matrix Adaptation Evolution Strategy (SVR-CMAES), were developed and assessed using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), and Global Performance Index (GPI). The accuracy of the hybrid SVR models was then compared against standalone Machine Learning (ML) and empirical models using limited meteorological data. Accordingly, the findings highlighted the superior accuracy of the SVR-PSO model in estimating ET0, followed closely by the SVR-DE and SVR-CMAES models. The outstanding performance of the SVR-PSO model was attributed to the inherent versatility and robustness of PSO, as well as its core social behaviour and swarm intelligence principles that allow for an exhaustive exploration of the solution space, thus enhancing the model's accuracy and reliability. In conclusion, the integration of SVR with the meta-heuristics algorithm represents a significant advancement in ET0 estimation models with enhanced accuracy. The study underlines the critical role of advanced hybrid models in enhancing ET0 prediction accuracy, thereby supporting the implementation of efficient water resource management and strategic planning across Malaysia.
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The datasets and models supporting the findings of this study are available from the corresponding author upon request.
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Stephen Luo Sheng Yong and Jing Lin Ng were responsible for the study's conception and manuscript writing. Data collection and analysis were carried out by Yuk Feng Huang and Chun Kit Ang. Norashikin Ahmad Kamal was responsible for refining the manuscript through comprehensive review and editing. Majid Mirzaei and Ali Najah Ahmed provided supervision, guiding the research direction and methodology. All authors have read and approved the final version of the manuscript.
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Highlights
• This study developed hybrid Support Vector Regression (SVR) models integrating with metaheuristic algorithms for ET0 estimation in Malaysia.
• The estimations were compared with empirical and standalone Machine Learning (ML) models.
• The SVR-PSO model was the most superior model for ET0 estimation, followed closely by SVR-DE and SVR-CMAES.
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Yong, S., Ng, J., Huang, Y. et al. Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models. Water Resour Manage (2024). https://doi.org/10.1007/s11269-024-03860-6
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DOI: https://doi.org/10.1007/s11269-024-03860-6