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.

自适应神经模糊推理系统 (ANFIS) 的机器学习方法被提出作为一种数据驱动技术来模拟露点温度 (DPT)。T min、T max 和 T mean 的输入模式用于训练。结果表明ANFIS方法能够以较高的准确度识别数据模式。然而，该方法表明，通过添加额外的功能，处理时间和计算机资源可能会大大增加。根据结果​​，如果包含新功能，迭代次数和计算资源可能会发生巨大变化。因此，必须在方法框架内优化调整参数。研究结果表明，所提出的机器学习方法的结果与观察到的数据高度一致。使用这个预测工具包，可以根据温度分布充分预测 DPT。该建模方法已被证明可用于预测各个站点的 DPT。此外，本研究在各种尺度上彻底比较了双层神经网络 (BNN) 和 ANFIS 模型，其中 ANFIS 模型对于几乎所有成员函数的数量都保持稳定。