## 1 Introduction  
Forest fires represent a severe threat to ecosystems, wildlife, and human safety, necessitating the development of reliable early prediction mechanisms. Wildfires have been a major concern due to their growing frequency and intensity, exacerbated by climate change and human activities [1]. Early detection and accurate prediction are crucial for minimizing damage and ensuring timely interventions by fire management teams. Traditionalapproaches to forest fire prediction have relied heavily on physical sensor deployments, which, while effective in localized areas, face limitations in scalability, cost, battery recharging, and coverage, particularly in large and remote regions [2]. To address such challenges, researchers have increasingly turned to advanced sensing and machine learning (ML) techniques to improve the accuracy and efficiency of forest fire prediction systems. ML models, particularly those leveraging deep learning approaches such as artificial neural networks (ANNs), have been applied to predict forest fire occurrences by analyzing patterns in environmental data collected through various sensors, such as temperature, humidity, and air quality measurements [3]. Artificial intelligence algorithms have proven effective in predicting forest fire risks by analyzing data from multiple sources and identifying key drivers. High-risk areas were mapped, providing a scientific basis for improving forest fire prevention and control strategies [4; 5; 6; 7]. These systems can process vast amounts of sensor data in real-time, providing critical insights into fire dynamics and enabling early warnings. However, the integration of such systems often requires significant infrastructure investment, including the deployment of a dense network of physical sensors, which may not be feasible in all environments, especially when remote and hostile. Our prior work introduced a smart and secure wireless sensor network (SSWSN) [8] capable of predicting forest fire scenarios using data collected from a network of physical sensors. The SSWSN demonstrated the ability to classify various fire-related scenarios with high accuracy, showcasing its potential as an innovative tool for forest fire management. This approach successfully addressed the challenges of traditional fire prediction methods by incorporating security measures to protect sensitive data and improve the robustness of the system. However, expanding such systems to larger and more complex environments remains challenging due to the physical constraints of deploying additional sensors, which can be costly and logistically difficult [9]. Advancing our previous study, our current work explores the integration of virtual sensors as a means to enhance the predictive capabilities of our SSWSN model. Virtual sensing revolutionizes wireless sensor networks (WSNs) by estimating unmeasured variables through computational models and related data. Using deterministic methods based on system dynamics or data-driven techniques like machine learning, virtual sensors enhance efficiency and adaptability in applications such as environmental monitoring, healthcare, and automation. This approach overcomes traditional sensing limitations, offering cost-effective, accurate, and intelligent solutions for complex systems [10; 11; 12]. Virtual sensors are synthetic data points generated through computational techniques, which emulate the functionality of physical sensors without requiring additional physical hardware. By incorporating these virtual sensors into the existing framework, we aim to investigate their potential for improving the model's performance in identifying and predicting fire scenarios more accurately. The use of virtual sensors not only mitigates the need for large-scale physical sensor deployment but also enhances the scalability and adaptability of fire prediction systems [13]. Virtual sensing is not only used in forest fire detection but also finds applications in building systems and various other IT domains [14; 15].