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Predicting Evapotranspiration Using Support Vector Machine Model and Hybrid Gamma Test

In: Application of Machine Learning Models in Agricultural and Meteorological Sciences

Author

Listed:
  • Mohammad Ehteram

    (Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering)

  • Akram Seifi

    (Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture)

  • Fatemeh Barzegari Banadkooki

    (Payame Noor University, Agricultural Department)

Abstract

In agriculture and water resource management, evapotranspiration prediction plays an important role. In this article, the optimized SVM models are used for predicting evapotranspiration. In this study, the SVM parameters are adjusted using particle swarm optimization (PSO), antlion optimization (ANO), and crow optimization algorithm (COA). For choosing the best input combination, a hybrid gamma test is used. Automatically, the hybrid gamma test can determine the best input combination. The optimized SVM models outperformed the standalone SVM models. The mean absolute error (MAE) of the SVM-ANO, SM-COA, SVM-PSO, and SVM models was 0.678, 0.789, 0.812, and 0.824 at the Iranshahr station.

Suggested Citation

  • Mohammad Ehteram & Akram Seifi & Fatemeh Barzegari Banadkooki, 2023. "Predicting Evapotranspiration Using Support Vector Machine Model and Hybrid Gamma Test," Springer Books, in: Application of Machine Learning Models in Agricultural and Meteorological Sciences, chapter 0, pages 131-145, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9733-4_14
    DOI: 10.1007/978-981-19-9733-4_14
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