Extreme learning machine based prediction of daily dew point temperature

The dew point temperature is a significant element particularly required in various hydrological, climatological and agronomical related researches. This study proposes an extreme learning machine (ELM)-based model for prediction of daily dew point temperature. As case studies, daily averaged measured weather data collected for two Iranian stations of Bandar Abass and Tabass, which enjoy different climate conditions, were used. The merit of the ELM model is evaluated against support vector machine (SVM) and artificial neural network (ANN) techniques. The findings from this research work demonstrate that the proposed ELM model enjoys much greater prediction capability than the SVM and ANN models so that it is capable of predicting daily dew point temperature with very favorable accuracy. For Tabass station, the mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R) achieved for the ELM model are 0.3240 °C, 0.5662 °C and 0.9933, respectively, while for the SVM model the values are 0.7561 °C, 1.0086 °C and 0.9784, respectively and for the ANN model are 1.0324 °C, 1.2589 °C and 0.9663, respectively. For Bandar Abass station, the MABE, RMSE and R for the ELM model are 0.5203 °C, 0.6709 °C and 0.9877, respectively whereas for the SVM model the values are 1.0413 °C, 1.2105 °C and 0.9733, and for the ANN model are 1.3205 °C, 1.5530 °C and 0.9617, respectively. The study results convincingly advocate that ELM can be employed as an efficient method to predict daily dew point temperature with much higher precision than the SVM and ANN techniques.