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Modeling of Relative Humidity Using Artificial Neural Network

Author

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  • Samer AlSadi
  • Tamer Khatib

Abstract

This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE are used to evaluate the neural networks. Based on results, the proposed neural network gives accurate prediction of hourly relative humidity whereas the MAPE, RMSE and MBE values in predicting hourly relative humidity are 5.08%, 5.8 and -0.041, respectively. While the MAPE values for the daily and monthly predicted values are 2.66% and 0.57%.

Suggested Citation

  • Samer AlSadi & Tamer Khatib, 2012. "Modeling of Relative Humidity Using Artificial Neural Network," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 2(2), pages 81-86.
    • Handle: RePEc:asi:joasrj:v:2:y:2012:i:2:p:81-86:id:3329
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    File URL: https://archive.aessweb.com/index.php/5003/article/view/3329/5339
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    Cited by:

    1. Masoud Noshadi & Hossein Ahani, 2015. "Focus on relative humidity trend in Iran and its relationship with temperature changes during 1960–2005," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 17(6), pages 1451-1469, December.

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