Relative humidity is a sensitive parameter in the sciences. It impacts the physical performance of electrical devices, metals, agro-food and biological items to name just a few. There are numerous factors such as dew point temperature, ambient temperature and solar radiation that can combine to influence relative humidity. There have been a handful of studies conducted on forecasting variations in relative humidity in the city of Biskra, Algeria. One typically finds that the dew point temperature is involved in variants of relative humidity, so we have been trying to predict it and create a semi-empirical equation as a function of apparent solar time, influenced by maximum and minimum dew point temperature, rather than create a new correlation related with ambient temperature. This study aims to contribute relative humidity as a function of the dew point temperature and validate it with experimental measurements.
The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ2 . Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ2 : 0.0159 – 0.0166 K2 ). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ2 =0.0163 and 0.0159 K2 ). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ2 =0.0189 K2 ).
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