# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 1 Introduction  
Forests are vital structures for the world, covering 33% of the land on Earth and contributing directly or indirectly to ecological, economic, and social life as the main source of rich biological diversity [1]. The destruction of forests disrupts the basic structure of the ecosystem and increases the level of carbon dioxide in the atmosphere. At the same time, the destruction of forests triggers global warming, disrupts the ecosystem, and reduces water resources. In order to prevent these situations, measures should be taken to reduce forest fires first. Forest fires are disasters that occur in open areas and tend to spread with the influence of wind; they are difficult to prevent and cause great damage due to the presence of easy-burning materials such as wood, leaves, and branches in the forest, which increases the effect of the fire. Contrary to popular belief, these fires not only destroy forests. Another harm of forest fires is that they cause respiratory and cardiovascular diseases in people due to smoke [2]. It is also a scientific fact that they cause an increase in disease and death rates in pets and livestock [3]. Another situation that forest fires negatively affect is the economy. During periods when forest fire extinguishing efforts were carried out in the USA, employment in the region increased by 1%. On the other hand, it was observed that there was a 20% increase in house prices after the forest fires [4]. Considering the effects of forest fires on the environment, a very sad picture has emerged. While the average annual number of forest fires between 2013 and 2022 was 61,410, the area affected by fire was 7.2 million decares on average, annually [5]. The delayed detection of forest fires may cause irreversible damage. The formation of a small fire primarily results in a large amount of smoke. This smoke harbors an upcoming forest fire. To prevent damage from a disaster, smoke, which signals the beginning of a fire, must be detected quickly and accurately. Difficulties lie in the complexity of the image background, the difficulty in distinguishing between features because of the cloud-like appearance of smoke, the inability to fully detect smoke in blurry images, the difficulty in detecting smoke because the objects behind it are more visible when the smoke is thin, and the difficulty in detecting the light intensity and angle of incidence in the open air. For many years, the detection of forest fires has been performed only with methods dependent on manpower such as observation towers, ground teams, emergency response vehicles, and telephone notifications. Due to the limitations of these methods, the precautions to be taken against fire risks have been limited. In recent years, forest fires have been monitored in real time using international satellite systems, and fire moment mapping has been performed. Along with technological developments, remote sensing techniques and satellite-based imaging systems have been used in fire detection since the 1990s; these developments were followed by the integration of artificial intelligence and deep learning-based approaches in the post-2010 period. Recently, architectures such as U-Net, Yolo, and LSTM have further accelerated the process. Especially in countries such as the USA, Australia, Canada, and China, forest fires are monitored in real time thanks to systems developed by NASA, CSIRO, and various academic institutions, and are transmitted to authorized units via automatic warning systems. These systems, developed with deep learning algorithms, have been developed to analyze fire smoke, temperature changes, and light signals, and early warning systems have been developed. Today, real image processing algorithms such as CNN and Yolo and time series-oriented artificial intelligence models such as LSTM are used to detect smoke and temperature anomalies in the initial stages of a fire. In this context, DL systems are one of the most important and innovative basic approaches for smoke detection in forest fires.