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Title: Estimating daily dew point temperature using machine learning algorithms
Authors: Qasem, SN
Samadianfard, S
Nahand, HS
Mosavi, A
Shamshirband, S
Chau, KW 
Keywords: Dew point temperature
Machine learning
Meteorological parameters
Statistical analysis
Big data
Gene expression programming (GEP)
Deep learning
M5 model tree
Support vector regression (SVR)
Hydrological model
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Water, 20 Mar. 2019, v. 11, no. 3, 582, p. 1-13 How to cite?
Journal: Water 
Abstract: In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (V-p), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R-2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96 degrees, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, V-p, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.
ISSN: 2073-4441
DOI: 10.3390/w11030582
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Qasem, S.N.; Samadianfard, S.; Sadri Nahand, H.; Mosavi, A.; Shamshirband, S.; Chau, K.-W. Estimating Daily Dew Point Temperature Using Machine Learning Algorithms. Water 2019, 11, 582, 13 pages is available at
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