## 1 Introduction  
### State of the Art  
The accurate estimation of dew point temperature (DPT) plays a crucial role in enhancing the efficiency and performance of various systems, ranging from meteorological forecasting to industrial applications, extending to the design of atmospheric water capture systems [1, 2]. The DPT refers to the temperature at which the atmosphere reaches saturation with water vapor, leading to the condensation of a portion of this suspended water [3]. The DPT is invariably lower than or equal to the ambient temperature. Consequently, precise determination of the DPT holds significant importance, as it governs heat stress on individuals, and monitors variations in evaporation rates and humidity trends [4]. Beyond its fundamental thermodynamic relevance, the dew point serves as a connection between theoretical understanding and practical applications across diverse fields such as agriculture, horticulture, and agronomy, where seasonal DPT trends inform irrigation strategies and optimize water usage, maximizing crop productivity [5]. Additionally, accurate weather pattern prediction relies on incorporating DPT calculation into models,providing insights for public safety, agricultural planning, and climate research [6]. Furthermore, DPT plays a crucial role in various engineering applications such as construction [7; 8] and industry. Controlling humidity through dew point manipulation is essential for ensuring product quality and process efficiency in diverse sectors like food processing [9], electronics manufacturing [10], or water harvesting [2; 11]. Additionally, most studies analyzing humid air in ambient conditions of temperature and pressure focus on partial pressure calculation, relegating the DPT as a secondary parameter [12; 13]. Nevertheless, the dew point is a key factor for different fields, especially, but not limited to, water harvesting, where cooling the air below its DPT consumes enormous energy and causes low energy efficiency, especially in hot and dry areas [14]. Regarding the specific DPT calculation, various methods for determining the DPT have been discussed, including empirical equations, depending on the operating conditions. Different correlations have been developed to determine properties of compressed humid air [15; 16; 17]. To date, the estimation of DPT in ambient conditions was traditionally performed using empirical equations [18]. In this regard, among the most common approaches, the rule of thumb for moist air suggests a 1 \({}^{\circ}\)C decrease in DPT for every 5% decrease in relative humidity, starting at the dry bulb temperature when RH = 100% [19]. However, these methods are limited in their accuracy, particularly under varied environmental conditions, including the spectrum of average conditions of temperature and humidity, but also very high temperatures (above 40 \({}^{\circ}\)C) or temperatures near 0 \({}^{\circ}\)C, where condensation becomes challenging. Additionally, conditions of very high relative humidity (above 80%) and very low relative humidity (below 15%) further complicate the accuracy. Combining extreme temperatures with extreme humidity levels presents the most difficult scenarios for accurately modeling the DPT. With the development of Machine Learning and Soft Computing Techniques (SCTs), more sophisticated models have been developed for DPT estimation. Support Vector Regression (SVR), optimized with algorithms such as Ant Colony Optimization (ACO), has been employed to balance accuracy and complexity, providing DPT estimations across different climatic conditions [20; 21]. Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs) have shown great potential in capturing nonlinear relationships between meteorological variables and DPT. Studies have demonstrated their effectiveness, particularly when integrated with optimization techniques like Genetic Algorithms (GAs) [22; 23].