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In this research, dew point temperature (DPT) is simulated using thedata-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilizedas a data-driven technique to forecast this parameter at Tabriz in EastAzerbaijan. Various input patterns, namely T min, T max, and T mean, areutilized for training the architecture whilst DPT is the model's output. Thefindings indicate that, in general, ANFIS method is capable of identifying datapatterns with a high degree of accuracy. However, the approach demonstratesthat processing time and computer resources may substantially increase byadding additional functions. Based on the results, the number of iterations andcomputing resources might change dramatically if new functionalities areincluded. As a result, tuning parameters have to be optimized inside the methodframework. The findings demonstrate a high agreement between results by thedata-driven technique (machine learning method) and the observed data. Usingthis prediction toolkit, DPT can be adequately forecasted solely based on thetemperature distribution of Tabriz. This kind of modeling is extremelypromising for predicting DPT at various sites. Besides, this study thoroughlycompares the Bilayered Neural Network (BNN) and ANFIS models on various scales.Whilst the ANFIS model is extremely stable for almost all numbers of membershipfunctions, the BNN model is highly sensitive to this scale factor to predictDPT.

Integration of neural network and fuzzy logic decision making compared  with bilayered neural network in the simulation of daily dew point  temperature