# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 1 Introduction  
Forest fires destroy numerous trees and cause the death or displacement of wild animals. They cause the destruction and modification of forest vegetation and structure, and of the ecological environment, climate, and soil properties, reducing the ability of the forest to prevent water and soil loss and to regulate the weather. Concurrently, they reduce the vegetation area, allowing the ground to receive direct solar radiation, making the surface temperature rise too quickly, further damaging ground creatures, and causing desertification and other hazards [1].Traditional forest fire monitoring is time-consuming and labor-intensive. Forest fire prevention has gradually shifted from manual patrols, lookout monitoring, air forest protection, satellite remote sensing, and other monitoring methods to early monitoring and warning systems for forest fires. Early warning systems and the timely detection of forest fires are crucial measures to reduce fire loss. Wireless sensor networks (WSNs) are a relatively stable technical means to obtain environmental information on combustibles under forests, monitor changes in the environmental micrometeorology before forest fires, and provide early warnings of imminent fires [2, 3]. Therefore, the WSN, as an early monitoring tool for forest fires, has become an effective means of real-time forest fire monitoring. Deep learning methods that analyze the forestcanopy image information from images collected above the forest have also been applied for dynamic forest fire monitoring/early warning systems. Both techniques are described in detail below. Before a forest fire, environmental information under the forest canopy can be collected using WSN technology, and changes in the forest environment micrometeorological data can be recorded and used to perceive signs of forest fires at an early stage. The generation of flame and smoke above the forest can be identified by applying deep learning to forest canopy images to realize fire-monitoring sampling and identify early warnings, reducing firefighting time and preventing major fires [4; 5; 6].The WSN monitors the meteorological factors of the forest environment and the moisture content of undergrowth combustibles, using this data to simulate the spread and propagation of forest fires. A forest fire early warning model was built, verified, and modified in an actual forest environment by analyzing these factors [7; 8; 9; 10]. [PERSON] et al. [11] built a forest fire-monitoring WSN system using temperature, humidity, and smoke concentration sensors to collect real-time forest environmental information. This system improved the DV-Hop location algorithm and the accuracy of the traditional iteration algorithm; optimized the distance estimation, node location, and model performance; and conducted field verification to meet the needs for early monitoring and positioning. [PERSON] [12] employed flame, smoke, temperature, and humidity sensors to monitor forest fires. In combination with the technical characteristics of ZigBee and long-range radio (LoRa), Zigbee technology was used for data transmission in the sensing area of the forest. By improving the LoRa relay-group network communication agreement and conducting field tests, [PERSON] collected and transmitted data for an intelligent forest fire early warning system. [PERSON] [13] applied WSN nodes to monitor the characteristic signals of forest fires and fused temperature, carbon monoxide concentration, and smoke information to obtain the trust function generated by forest fires. The field test analyzed the probability of a forest fire. The results demonstrated that the system can reliably set off an alarm when the collected information exceeds a set threshold, quickly and accurately identifying the probability of a forest fire. Moreover, WSNs can collect real-time information, but using them to perceive the current fire situation in a forest canopy promptly is difficult.