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Predicting Dew Point Using Optimized Least Square Support Vector Machine Models

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

Dew point prediction (DPT) is an important topic in agriculture and water resource management. In this chapter, robust soft computing models are used for estimating DPT. This study uses a standalone least square support vector machine (LSSVM) and LSSVM models to estimate the DPT. In this chapter, the LSSVM-antlion optimization algorithm (ANOA), LSSVM-dragonfly algorithm (DOA), LSSVM-crow optimization algorithm (LSSVM-COA), and LSSVM were used to estimate DPT. The different input combinations were used to predict DPT. The results indicated that the optimized LSSVM outperformed the LSSVM models. The best input variable consisted of input variables of relative humidity (RHU), average temperature (AVTEM), wind speed (WIPSE), and number of sunny hours (NOSH). The results indicated that the optimized LSSVM models outperformed the LSSVM models.

Suggested Citation

  • Mohammad Ehteram & Akram Seifi & Fatemeh Barzegari Banadkooki, 2023. "Predicting Dew Point Using Optimized Least Square Support Vector Machine Models," Springer Books, in: Application of Machine Learning Models in Agricultural and Meteorological Sciences, chapter 0, pages 187-196, Springer.
    • Handle: RePEc:spr:sprchp:978-981-19-9733-4_18
    DOI: 10.1007/978-981-19-9733-4_18
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