# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 1 Introduction  
Given its direct impact on public safety and the environment, early fire detection is a difficult but crucial problem. To avoid harm and property damage, advanced technology requires appropriate methods for detecting fires as soon as possible [1]. According to UNEP, \"Wildfires are becoming more intense and more frequent, ravaging communities and ecosystems in their path\" [2]. Wildfires continue to burn for days without swift action, resulting in a climate crisis and the loss of lives. Due to the climate crisis, the world is starting to face anonymous fluctuations in water levels, changes in temperature, and the extinction of some protected animals, which will affect the balance of life in the future. Therefore, we must take wildfire problems seriously before they become catastrophic. Installing early fire detection in the forest and an automatic notification system to notify the fire department can reduce countless problems. It has always been a challenge to control fires on a global scale. In 2019, there were 40,030 fires in South Korea, resulting in 284 deaths and 2219 injuries, according to the Korean National Fire Agency. In addition, property damage totaled KRW 2.2 billion as a result of 110 fires and 0.8 fire-related deaths daily. Two significant Korean cities experienced fires in 2020 that resulted in the deaths of over 50 people in each location. A 33-story tower block building in Ulsan burned down, and a warehouse fire broke out in Incheon [3]. However, these incidents are just in Korea. Imagine having a wildfire that is the size of a country continuing on for weeks. According to UNEP, the intensity and frequency of wildfires are increasing, wreaking havoc on the ecosystems and populations they pass through. These wildfires happen for many reasons. It is either due to bushfires that are caused naturally or to human errors. If bushfires are detected early, the spreading of wildfires can be prevented. Furthermore, if we can stop people from starting a fire as they light it, we can prevent further incidents. In our research, we found some studies on fire alarms that are installed in the forest. However, we found some challenges they may face in our own study. * Sensors are one of the widely used techniques to determine whether there is a fire or not. We found that just using a smoke detection sensor can lead to false alarms because some of the smells can come from different places or from someone smoking. * Remote cameras are used in some of the other fire detection systems to determine whether there is a fire or not. This kind of surveillance requires human employees to continuously monitor the cameras, which can sometimes lead to the employees falling asleep. * R-CNN-based approaches are also efficiently used to identify and eliminate fire catastrophes. However, this method might also make errors sometimes and incorrectly classify candidate fire regions as real fires. Therefore, they may also lead to false alarms. Identifying these previous errors made by other research and companies gave us innovative ideas. When humans eliminate one of their sensory features, other sensory features become more enhanced. For example, when you blindfold someone, other senses in their body enhance. This realization came through serendipity. Having walked by an air purifier multiple times after a workout, the air purifier worked extra hard. Then, we realized the machine could not see but could smell us first. Our human body can smell things first before it can see. Another example is when leaving a pot of soup boiling for too long, you can smell the burnt scent before you can see it. However, we humans double-check to see what is burning. We wanted our ideas to do both so we converged the ideas from all the research into one product that would prevent wildfires from happening. The rest of this manuscript is structured into five more sections. Section 2 reviews the literature on traditional and deep learning methods used to identify particular fire regions.