Modeling of Relative Humidity Using Artificial Neural Network

Samer AlSadi and Tamer Khatib

Journal of Asian Scientific Research, 2012, vol. 2, issue 2, 81-86

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%.

Keywords: Relative humidity; Metrological variables prediction; ANN (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://archive.aessweb.com/index.php/5003/article/view/3329/5339 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:asi:joasrj:v:2:y:2012:i:2:p:81-86:id:3329

Access Statistics for this article

More articles in Journal of Asian Scientific Research from Asian Economic and Social Society
Bibliographic data for series maintained by Robert Allen ().