Evaluating GMDH-based models to predict daily dew point temperature (case study of Kerman province)
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ORIGINAL PAPER
Evaluating GMDH‑based models to predict daily dew point temperature (case study of Kerman province) Kourosh Qaderi1 · Bahram Bakhtiari1 · Mohamad Reza Madadi2 · Zahra Afzali‑Gorouh3 Received: 9 May 2019 / Accepted: 11 November 2019 © Springer-Verlag GmbH Austria, part of Springer Nature 2019
Abstract Accurate prediction of dew point temperature is very important in decision making in many fields of water resources planning and management, agricultural engineering and climatology. This study investigates the ability of some data-driven models (DDMs) in predicting daily dew point temperature. These models include traditional group method of data handling (GMDH), improved GMDH models (GMDH1, GMDH2), and two hybrid GMDH-based models (GMDH-HS and GMDHSCE) which were developed by combination of GMDH with two optimization algorithms, harmony search (HS) and shuffled complex evolution (SCE). 11 years of daily recorded weather variables at Kerman synoptic station including mean temperature (Ta), sunshine hours (S), soil temperature (Ts), mean relative humidity (Rh), and wind speed (Ws) were used to evaluate the proficiency of developed models. Sensitivity analysis revealed that Rh is the most influential input variable in predicting dew point temperature. Seven quantitative standard statistical indices including coefficient of efficiency (CE), correlation coefficient (CC), root mean square error (RMSE), mean square relative error (MSRE), mean absolute percentage error (MAPE), relative bias (RB) and threshold statistic ( TSx) were employed to examine the performance of applied models. The results indicated the superiority of combinatorial models (GMDH-HS and GMDH-SCE) to the other developed models in predicting the dew point temperature (Tdp). In terms of threshold statistic, GMDH2-HS had the highest values of TSx (the best model) and GMDH2-SCE, GMDH1-HS, GMDH1-SCE, GMDH2 and GMDH1 got the next ranks, respectively. It was observed that GMDH2-HS could predict the Tdp (with CE = 0.979 and RMSE = 0.745) better than the other models (with CE = 0.958 and RMSE = 0.932, in average), indicating its high efficiency.
1 Introduction Dew point temperature (Tdp) is the temperature at which the water vapor in air condenses into liquid water at the same rate at which it evaporates. Tdp is one of the key input parameters in wide spread models to estimate evaporation and evapotranspiration (Hubbard et al. 2003), actual vapor pressure or relative humidity (Mahmood and Hubbard 2005), amount of available moisture in the air (Shank Responsible Editor: E.-K. Jin. * Kourosh Qaderi [email protected] 1
Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
2
Department of Water Engineering, University of Jiroft, Jiroft, Iran
3
Department of Water Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
et al. 2008), near surface humidity, and frost and ambient temperature. Also it has been proved that dew point temperature changes may have significant implication for climate cha
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