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Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm

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  • Mohammadi, Babak
  • Mehdizadeh, Saeid

Abstract

In achieving water resource management goals such as irrigation scheduling, an accurate estimate of reference evapotranspiration (ET0) is critical. Support vector regression (SVR) was applied to the modeling of daily ET0 at three meteorological stations in Iran subject to different climates: Isfahan (arid), Urmia (semi-arid), and Yazd (hyper-arid). Different pre-processing approaches [relief (RL), random forests (RF), principal component analysis (PCA), and Pearson's correlation (COR)] served to determine the SVR’s optimal input combinations. While these approaches introduced different inputs to the SVR models, those drawn upon by the RF approach (i.e., RF-SVR) generated better results than other approaches. Models performance was evaluated using the root mean square error (RMSE), normalized RMSE (NRMSE), mean absolute error (MAE), coefficient of determination (R2), and the Nash-Sutcliffe efficiency (E). A novel hybrid model, coupling SVR with a whale optimization algorithm (WOA), was also developed and applied to daily ET0 modeling. The hybrid models outperformed the SVR-only models, with the hybrid RF-SVR-WOA model having the best performance.

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  • Mohammadi, Babak & Mehdizadeh, Saeid, 2020. "Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:agiwat:v:237:y:2020:i:c:s0378377419316944
    DOI: 10.1016/j.agwat.2020.106145
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    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.
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    14. Phon Sheng Hou & Lokman Mohd Fadzil & Selvakumar Manickam & Mahmood A. Al-Shareeda, 2023. "Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    15. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    16. Valipour, Mohammad & Khoshkam, Helaleh & Bateni, Sayed M. & Jun, Changhyun & Band, Shahab S., 2023. "Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States," Agricultural Water Management, Elsevier, vol. 283(C).
    17. Hadeel E. Khairan & Salah L. Zubaidi & Mustafa Al-Mukhtar & Anmar Dulaimi & Hussein Al-Bugharbee & Furat A. Al-Faraj & Hussein Mohammed Ridha, 2023. "Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
    18. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    19. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).
    20. Fatemeh Rezaie & Mahdi Panahi & Sayed M. Bateni & Changhyun Jun & Christopher M. U. Neale & Saro Lee, 2022. "Novel hybrid models by coupling support vector regression (SVR) with meta-heuristic algorithms (WOA and GWO) for flood susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1247-1283, November.
    21. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    22. Farshad Ahmadi & Saeid Mehdizadeh & Babak Mohammadi, 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4127-4147, September.

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