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An insight into the estimation of relative humidity of air using artificial intelligence schemes

Author

Listed:
  • Mahdi Ghadiri

    (Institute of Research and Development, Duy Tan University
    The Faculty of Environment and Chemical Engineering, Duy Tan University)

  • Azam Marjani

    (Ton Duc Thang University
    Faculty of Applied Sciences, Ton Duc Thang University)

  • Samira Mohammadinia

    (Islamic Azad University, Mahshahr Branch)

  • Saeed Shirazian

    (South Ural State University)

Abstract

The present work suggested predicting models based on machine learning algorithms including the least square support vector machine (LSSVM), artificial neural network (ANN), and adaptive network-based fuzzy inference system (ANFIS) to calculate relative humidity as function of wet bulb depression and dry bulb temperature. These models are evaluated based on several statistical analyses between the real and determined data points. Outcomes from the suggested models expressed their high abilities to determine relative humidity for various ranges of dry bulb temperatures and also wet-bulb depression. According to the determined values of MRE and MSE were 0.933 and 0.134, 2.39 and 1, 1.291 and 0.193, 0.931 and 0.132 for the RBF-ANN, MLP-ANN, ANFIS, and LSSVM models, respectively. The aforementioned predictors have interesting value for the engineers and researchers who dealing with especial topics of air conditioning and wet cooling towers systems which measure the relative humidity.

Suggested Citation

  • Mahdi Ghadiri & Azam Marjani & Samira Mohammadinia & Saeed Shirazian, 2021. "An insight into the estimation of relative humidity of air using artificial intelligence schemes," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10194-10222, July.
    • Handle: RePEc:spr:endesu:v:23:y:2021:i:7:d:10.1007_s10668-020-01053-w
    DOI: 10.1007/s10668-020-01053-w
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    References listed on IDEAS

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    1. Jens Baetens & Greet Van Eetvelde & Gert Lemmens & Nezmin Kayedpour & Jeroen D. M. De Kooning & Lieven Vandevelde, 2019. "Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Tao Wu & Jinde Cao & Lianglin Xiong & Haiyang Zhang, 2019. "New Stabilization Results for Semi-Markov Chaotic Systems with Fuzzy Sampled-Data Control," Complexity, Hindawi, vol. 2019, pages 1-15, November.
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    Cited by:

    1. Rana Muhammad Adnan & Sarita Gajbhiye Meshram & Reham R. Mostafa & Abu Reza Md. Towfiqul Islam & S. I. Abba & Francis Andorful & Zhihuan Chen, 2023. "Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-29, March.

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