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An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration

Author

Listed:
  • Mohammad Ehteram
  • Vijay P Singh
  • Ahmad Ferdowsi
  • Sayed Farhad Mousavi
  • Saeed Farzin
  • Hojat Karami
  • Nuruol Syuhadaa Mohd
  • Haitham Abdulmohsin Afan
  • Sai Hin Lai
  • Ozgur Kisi
  • M A Malek
  • Ali Najah Ahmed
  • Ahmed El-Shafie

Abstract

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models.

Suggested Citation

  • Mohammad Ehteram & Vijay P Singh & Ahmad Ferdowsi & Sayed Farhad Mousavi & Saeed Farzin & Hojat Karami & Nuruol Syuhadaa Mohd & Haitham Abdulmohsin Afan & Sai Hin Lai & Ozgur Kisi & M A Malek & Ali Na, 2019. "An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-25, May.
    • Handle: RePEc:plo:pone00:0217499
    DOI: 10.1371/journal.pone.0217499
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    Cited by:

    1. Armin Mahmoodi & Leila Hashemi & Milad Jasemi & Soroush Mehraban & Jeremy Laliberté & Richard C. Millar, 2023. "A developed stock price forecasting model using support vector machine combined with metaheuristic algorithms," OPSEARCH, Springer;Operational Research Society of India, vol. 60(1), pages 59-86, March.
    2. Fatemeh Barzegari Banadkooki & Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Wani M. Ridwam & Ahmed Sefelnasr & Ahmed El-Shafie, 2019. "Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    3. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    4. Mohammad Ehteram & Ali Najah Ahmed & Ming Fai Chow & Sarmad Dashti Latif & Kwok-wing Chau & Kai Lun Chong & Ahmed El-Shafie, 2023. "Optimal operation of hydropower reservoirs under climate change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10627-10659, October.
    5. 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).

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