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Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

1
Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
2
Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz, Yemen
3
Department of Water Engineering, University of Tabriz, Tabriz 5166616471, Iran
4
School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
5
Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
6
The Queensland University of Technology, Institute of Health and Biomedical Innovation, 60 Musk Avenue, Queensland 4059, Australia
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
8
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
9
Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(3), 582; https://doi.org/10.3390/w11030582
Received: 4 February 2019 / Revised: 13 March 2019 / Accepted: 18 March 2019 / Published: 20 March 2019
(This article belongs to the Section Hydrology)
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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 (Vp), 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 (R2) 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°, 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, Vp, 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. View Full-Text
Keywords: dew point temperature; prediction; machine learning; meteorological parameters; statistical analysis; big data; gene expression programming (GEP); deep learning; forecasting; M5 model tree; support vector regression (SVR); hydrological model; hydroinformatics; hydrology dew point temperature; prediction; machine learning; meteorological parameters; statistical analysis; big data; gene expression programming (GEP); deep learning; forecasting; M5 model tree; support vector regression (SVR); hydrological model; hydroinformatics; hydrology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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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.

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