## 1 Introduction  
The increase in extreme weather events, largely driven by global climate change, has intensified the issue of airport pavement icing. This problem presents serious challenges to aviation safety, as icy runways increase the risk of flight delays, cancellations, and significant safety hazards for both passengers and crew. As a result, understanding the mechanisms of airport pavement icing and developing reliable prediction tools are essential for maintaining safety and operational efficiency in airport activities [1]. Unlike ordinary roads, airport pavements are more vulnerable to complex icing conditions due to their exposure to open areas. These conditions are significantly affected by factors such as the wind speed, temperature, and humidity. Moreover, the use of deicing agents alters the thermal properties of the pavement, making the icing process more unpredictable and further complicating the accuracy of existing prediction models. Given these complexities, research focusing on airport pavement icing is imperative [2]. The need for efficient and simplified icing prediction tools is particularly urgent due to the operational challenges airport operators face during adverse weather conditions. The existingtools often underperform in the saline environments typical of airports, especially due to the presence of deicing agents. This highlights the importance of rigorously validated, experimentally derived, simplified prediction models. In recent years, data-driven approaches have shown promise in predicting road icing, particularly through the use of machine learning models that correlate meteorological factors--such as the temperature, humidity, precipitation, radiation, and wind speed--with the pavement temperature or icing conditions. Simple machine learning models were employed to establish this mapping relationship, generally based on basic models such as Multilayer Perceptron (MLP) [3; 4] and Decision Tree [5]. However, these models often capture only the direct relationships between inputs and outputs, neglecting the temporal dependencies and sequential patterns that are crucial for accurate prediction [6]. Theicing process, characterized by fluctuations in the surface temperature and ice thickness, clearly exhibits temporal dynamics. To address these issues, more advanced models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks [7] have been widely adopted. These models can retain historical data within the network, enabling more effective processing of sequential data and a better understanding of temporal dependencies [8]. In recent years, enhanced variants of LSTM networks, such as Convolutional Long Short-Term Memory (Conv-LSTM) [9] and Sequence-to-Sequence (Seq2 Seq) models [10], have gained popularity due to their ability to more effectively capture the spatial features of data. Despite these advances, the effectiveness of data-driven methods remains limited by the scarcity of high-quality data, particularly in diverse airport scenarios. These methods are more commonly applied to road icing prediction problems, as highways enable the collection of large amounts of data [11; 12]. However, the reliance on extensive datasets--which are often unavailable for specific airport conditions--presents a significant challenge for accurate decision-making by airport operators [13]. On the other hand, theoretical analysis methods provide crucial insights into the physical mechanisms of icing through heat transfer and phase change theories. Basic models concentrate on the phase transitions between water and ice under specific meteorological conditions, typically using input data such as the meteorological conditions and the properties of road materials. The outputs, including the surface temperature and ice thickness, are often derived using finite element or finite difference methods [2; 14].