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

Abstract

The machine learning method of Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed as a data-driven technique to model the dew point temperature (DPT). The input patterns, of T min, T max, and T mean, are utilized for the training. The results indicate thatANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the proposed machine learning method and the observed data. Using this prediction toolkit, DPT can be adequately predicted based on the temperature distribution. The modeling approach has shown to be promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales where the ANFIS model remains stable for almost all the numbers of the membership functions.