## 1 Introduction  
Heat release rate (HRR) is one of the most critical parameters for analyzing fire behavior, and it is a key indicator of fire's scale and growth [1, 2, 3, 4]. Furthermore, HRR is a crucial factor in fire risk assessment and in the development of firefighting and rescue strategies, directly affecting the effectiveness of fire suppression and rescue operations [5, 6, 7]. The HRR of various combustible materials is typically measured under laboratory settings using various cone calorimeters, and several studies have focused on improving the reliability of these measurements [8, 9, 10]. Although this approach can provide precise HRR measurements, the significant temporal, spatial, and technical resource requirements limit its widespread use in various fire scenarios. Therefore, many studies on HRR prediction using various approaches have been conducted to overcome the aforementioned limitations and facilitate more accessible HRR predictions [11; 12]. Previous research has proposed various empirical correlations to predict HRR by analyzing relationships between combustion characteristics and HRR. [PERSON] [13] developed a general correlation to predict the mean flame height of buoyant turbulent flames based on the relationship between flame characteristics and heat release parameters. This correlation was validated across a wide range of fire conditions and has since been widely used to estimate flame heights in various fire scenarios. Based on various fire experiments, [PERSON] [14] developed a correlation expressing the ratio of flame height to fire diameter as a function of the non-dimensional heat release rate. This formulation has proven robust across a broad range of fire conditions. Furthermore, [PERSON] et al. [15] developed a modified empirical correlation to estimate the HRR in pool fires under various pressure conditions. This study focused on n-heptane pool fires in a ventilated altitude chamber and highlighted the significant effect of ambient pressure on both the mass burning rate and the HRR. By incorporating a pressure correction factor into the oxygen-consumption-based HRR calculation, the results revealed that the HRR markedly increases with pressure. [PERSON] et al. [16] developed an empirical correlation to estimate the HRR in large-scale cable fires based on the relationship between cable composition and combustion characteristics. This study demonstrated a significant improvement in HRR prediction accuracy in complex fire scenarios by considering the physical and chemical properties of cables and their burning behavior. [PERSON] et al. [17] proposed an HRR prediction correlation for designing fire sources to evaluate the effects of design parameters on the tunnel fire temperature field. Fire experiments were conducted using oil-filled square burners of various sizes, and an empirical correlation between HRR and burner area was derived. Despite advancements in empirical correlations for HRR prediction, these methods require experimentally measured information such as fuel properties, fire conditions, and flame characteristics, which limit their practical application. Consequently, artificial intelligence (AI) models have attracted increasing attention for predicting fire-related characteristics. However, most AI-based studies have focused on fire detection and segmentation for smart fire protection [18; 19; 20; 21]. [PERSON] et al. [22] proposed a You Only Look Once model (YOLOv5) with coordinate attention, Swin Transformers, and feature fusion (YOLOv5-CSF); this is an improved fire detection model based on the YOLOv5 architecture. YOLOv5-CSF integrates coordinate attention blocks, Swin Transformer modules, and adaptive spatial feature fusion, enhancing the detection performance of the model for small and complex flames. This model outperformed traditional models in terms of the mean average precision. [PERSON] et al. [23] proposed a fire and smoke detection model based on a detection transformer framework enhanced by a convolutional neural network backbone.