Please use this identifier to cite or link to this item: https://scholar.ptuk.edu.ps/handle/123456789/219
Title: Modeling of Relative Humidity Using Artificial Neural Network
Authors: Alsadi, Samer
Khatib, Tamer
Keywords: Relative Humidity; Metrological Variables Prediction; ANN
Issue Date: 19-Feb-2012
Publisher: Asian Economic and Social Society
Series/Report no.: Journal of Asian Scientific Research;Vol.2, No.2, pp.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%.
URI: https://scholar.ptuk.edu.ps/handle/123456789/219
ISSN: 19921454
Appears in Collections:Engineering and Technology Faculty

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