## 1 Introduction
### State of the Art
Likewise, hybrid models, which combine different computational techniques, are also employed, enhancing the accuracy of DPT estimation [21]. Additionally, studies have shown that integrating Genetic Algorithms (GAs) to optimize the parameters of Least Square Support Vector Machine (LSSVM) and ANFIS can significantly improve the predictive performance, making these models valuable tools for practical applications [24]. While these previous works [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27] discuss methods and approximations, there are no specific equations or models using only ambient temperature and relative humidity as single inputs for calculating water DPT. Furthermore, soft computing models do not explicitly reveal the correlation between input and output data, making it difficult to understand the relationships and reasoning processes. In contrast, mathematical approaches or physical-based equations provide clear and explicit correlations between inputs and outputs, offering a transparent justification of the results. While SCT models are effective, their lack of interpretability can be a significant drawback in applications requiring clear understanding and justification of the predictions [25]. Late studies exploring hybrid SCTs highlight the importance of minimizing computational demands while maintaining high accuracy, which is crucial for practical applications [26; 27]. Computational demand refers to the amount of resources, such as processing time and memory, required to perform a task. Simplifying an equation does not significantly reduce computational load on modern processors, but simplicity can enhance usability and reduce the risk of errors. For instance, adding a single mathematical operation may have a minimal effect on a one-time calculation, but can significantly increase the load depending on the type of operation and the ongoing process. Considering addition/subtraction as the elemental baseline of computing, taking around 1 ns per operation, other operations require more time: multiplication takes 5-10 times more, division 10-20 times more, exponentiation 20-30 times more for small exponents (and much higher for large exponents), square roots 30-40 times more, logarithms 50-100 times more, and trigonometric functions 100-200 times more. These differences are further influenced by the specific algorithms and implementation strategies used in software and platforms, with efficient algorithms significantly enhancing performance [28; 29]. Furthermore, iterative calculations, optimization problems, and especially nested operations can repeat these operations dozens to thousands of times. Using RAM for storing intermediate results offers the fastest access with times in the order of nanoseconds to microseconds. However, this approach can be constrained by the available memory, particularly for large-scale computations, where writing results to temporary files can introduce considerable overhead, increasing access times to milliseconds, which is approximately 1000 times slower [30]. Therefore, reducing and replacing operations correctly can significantly reduce computing time. Recent works have emphasized that developing new, accurate, and reliable dew point equations for scientific and practical applications would contribute to applied thermal engineering and energy-efficient system designs [21].