Estimating daily dew point temperature using machine learning algorithms

Sultan Noman Qasem, Saeed Samadianfard, Hamed Sadri Nahand, Amir Mosavi, Shahaboddin Shamshirband, Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

81 Citations (Scopus)


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.

Original languageEnglish
Article number582
JournalWater (Switzerland)
Issue number3
Publication statusPublished - 1 Mar 2019


  • Big data
  • Deep learning
  • Dew point temperature
  • Forecasting
  • Gene expression programming (GEP)
  • Hydroinformatics
  • Hydrological model
  • Hydrology
  • M5 model tree
  • Machine learning
  • Meteorological parameters
  • Prediction
  • Statistical analysis
  • Support vector regression (SVR)

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology


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