**4. Machine Learning Methods**

### *4.1. Adaptive Neuro-Fuzzy Inference System*

Jang et al. [55] introduced the fuzzy adaptive system of adaptive neurology, called ANFIS, as an improved ML method and a data-driven modeling approach to evaluate the behavior of complex dynamic systems [56,57]. ANFIS aims to systematically generate unknown fuzzy rules from a given set of input and output data. ANFIS creates a functional map that approximates the internal system parameter estimation method [58–60]. Fuzzy systems are rule-based systems developed from a set of language rules. These systems can represent any system with good accuracy and are, therefore, considered to be universal approximators. Thus, ANFIS is the most popular neuro-fuzzy hybrid network used for the modeling of complex systems. The ANFIS model's main strength is that it is a universal approximator with the ability to request interpretable "if–then" rules [61]. In ANFIS, a Sugeno-type fuzzy system was used to construct the five-layer network.
