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

TL;DR

This paper compares the effectiveness of ANFIS and bilayered neural networks in predicting daily dew point temperature, demonstrating ANFIS's high accuracy and stability, with considerations for computational resources and parameter tuning.

Contribution

It introduces a comparative analysis of ANFIS and BNN models for dew point prediction, highlighting ANFIS's robustness and the impact of model parameters on performance.

Findings

ANFIS accurately predicts dew point temperature with high stability.

BNN's performance is highly sensitive to the number of membership functions.

Increased model complexity affects processing time and resource requirements.

Abstract

In this research, dew point temperature (DPT) is simulated using the data-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized as a data-driven technique to forecast this parameter at Tabriz in East Azerbaijan. Various input patterns, namely T min, T max, and T mean, are utilized for training the architecture whilst DPT is the model's output. The findings indicate that, in general, ANFIS 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 data-driven technique (machine learning method) and the observed data. Using this prediction toolkit, DPT can be adequately forecasted solely based on the temperature distribution of Tabriz. This kind of modeling is extremely promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales. Whilst the ANFIS model is extremely stable for almost all numbers of membership functions, the BNN model is highly sensitive to this scale factor to predict DPT.