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Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models

Author

Listed:
  • Stephen Luo Sheng Yong

    (UCSI University)

  • Jing Lin Ng

    (Universiti Teknologi MARA (UiTM))

  • Yuk Feng Huang

    (Universiti Tunku Abdul Rahman)

  • Chun Kit Ang

    (UCSI University)

  • Norashikin Ahmad Kamal

    (Universiti Teknologi MARA (UiTM))

  • Majid Mirzaei

    (University of Maryland)

  • Ali Najah Ahmed

    (Sunway University)

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.

Suggested Citation

  • Stephen Luo Sheng Yong & Jing Lin Ng & Yuk Feng Huang & Chun Kit Ang & Norashikin Ahmad Kamal & Majid Mirzaei & Ali Najah Ahmed, 2024. "Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4213-4241, September.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:11:d:10.1007_s11269-024-03860-6
    DOI: 10.1007/s11269-024-03860-6
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    References listed on IDEAS

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    1. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    2. Yash Agrawal & Manoranjan Kumar & Supriya Ananthakrishnan & Gopalakrishnan Kumarapuram, 2022. "Evapotranspiration Modeling Using Different Tree Based Ensembled Machine Learning Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1025-1042, February.
    3. Ahmadi, Farshad & Mehdizadeh, Saeid & Mohammadi, Babak & Pham, Quoc Bao & DOAN, Thi Ngoc Canh & Vo, Ngoc Duong, 2021. "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation," Agricultural Water Management, Elsevier, vol. 244(C).
    4. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.
    5. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    6. Junaid Maqsood & Aitazaz A. Farooque & Farhat Abbas & Travis Esau & Xander Wang & Bishnu Acharya & Hassan Afzaal, 2022. "Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 835-851, February.
    7. Alexandris, S. & Kerkides, P., 2003. "New empirical formula for hourly estimations of reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 60(3), pages 157-180, May.
    8. Roy, Dilip Kumar & Lal, Alvin & Sarker, Khokan Kumer & Saha, Kowshik Kumar & Datta, Bithin, 2021. "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system," Agricultural Water Management, Elsevier, vol. 255(C).
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