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

arXiv: https://arxiv.org/abs/2202.12256 · HTML: https://mlsanity.com/paper/2202.12256/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics](https://mlsanity.com/paper/2004.01509/) (2020)
- [Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources](https://mlsanity.com/paper/2012.05795/) (2020)
- [Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran](https://mlsanity.com/paper/2007.12475/) (2020)
- [Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm](https://mlsanity.com/paper/2002.06226/) (2020)
- [Developing an ANFIS PSO Model to Estimate Mercury Emission in Combustion Flue Gases](https://mlsanity.com/paper/1910.05118/) (2019)
- [Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model](https://mlsanity.com/paper/1907.04913/) (2019)
- [Evaluation of electrical efficiency of photovoltaic thermal solar collector](https://mlsanity.com/paper/2002.05542/) (2020)

## Abstract (arXiv)

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.