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 GMDH-SCE) 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.

中文翻译：

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评估基于 GMDH 的模型来预测每日露点温度（克尔曼省的案例研究）

露点温度的准确预测在水资源规划和管理、农业工程和气候学等许多领域的决策中非常重要。本研究调查了一些数据驱动模型 (DDM) 在预测每日露点温度方面的能力。这些模型包括传统的数据处理组方法（GMDH）、改进的GMDH模型（GMDH1、GMDH2），以及GMDH与两种优化算法结合开发的两种基于GMDH的混合模型（GMDH-HS和GMDH-SCE），和谐搜索 (HS) 和洗牌复杂进化 (SCE)。使用 Kerman 天气站 11 年每日记录的天气变量，包括平均温度 (Ta)、日照时数 (S)、土壤温度 (Ts)、平均相对湿度 (Rh) 和风速 (Ws) 来评估开发的模型。敏感性分析表明，Rh 是预测露点温度最有影响的输入变量。包括效率系数（CE）、相关系数（CC）、均方根误差（RMSE）、均方相对误差（MSRE）、平均绝对百分比误差（MAPE）、相对偏差（RB）和阈值七个定量标准统计指标使用统计量 (TSx) 来检查应用模型的性能。结果表明组合模型（GMDH-HS 和 GMDH-SCE）在预测露点温度 (Tdp) 方面优于其他开发的模型。在阈值统计方面，GMDH2-HS 具有最高的 TSx 值（最佳模型）和 GMDH2-SCE，GMDH1-HS、GMDH1-SCE、GMDH2 和 GMDH1 分别排在第二位。据观察，GMDH2-HS 可以预测 Tdp（CE = 0。