# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 1 Introduction
Humans have difficulty reaching and deploying WSN monitoring nodes in certain forest environments, such as steep terrains. When the tree crown catches fire, monitoring the state of the forest fire under the trees is challenging. Therefore, many scholars have used deep learning methods to recognize the forest canopy image information from fire images collected above the forest to identify fire danger as early as possible [14]. Uncrewed aerial vehicles (UAVs) and video surveillance can obtain forest canopy image information using deep learning methods for fire monitoring. The complex features of the forest fire image, such as smoke and flame, are analyzed to build a forest fire monitoring/early warning model [15; 16; 17; 18]. Furthermore, forest fire risk prediction models have been created to predict the time of fire occurrence, fire-spread direction, and fire intensity using deep learning to analyze various factors affecting forest fire [19; 20; 21; 22; 23]. [PERSON] et al. [24] proposed a semi-supervised classification model using support vector machine technology to divide forest areas into high, medium, and low activity based on the likelihood of a forest fire in an area. The model's forest fire identification is satisfactory, with a prediction accuracy of 90%. [PERSON] et al. [25] determined a forest fire risk index based on the forest area and vegetation at Mount Tai, and divided the monitoring area into high-, medium-, and low-risk areas on a fire-risk rating map. Based on the sensitive characteristics of chaotic systems, the theory of differential flatness was adopted to track and control UAVs, realizing random path planning. [PERSON] et al. [26] employed data enhancement technology to enhance the data features of forest fire images and mark candidate boxes on target detection datasets. Threeconvolutional neural networks(VGG16, Inception V3, and ResNet50)were applied to build a forest fire early warning model using migration technology. The model parameters were gradually optimized, and their accuracy was verified. This method can accurately detect the fire area in an image and provide fire location information. [PERSON] et al. [27] combined cellular automata with existing forest fire models to build an improved forest fire spread model. The model calculates the speed change rate index according to meteorological factors affecting the forest fire spread, and adaptively adjusts the time steps of the cellular automata through a speed change rate index. The model can be used to simulate and predict forest fire spread. The experimental data reveal that the fire-prediction accuracy of the model output reaches the fire-detection accuracy of real-time prediction. Thus, deep learning methods are highly suitable for forest fire identification [28; 29; 30; 31; 32; 33]. However, in practical applications, most deep learning methods suffer from low accuracy in identifying forest fires. The low efficiency of complex upper-layer feature extraction of fire images results in low robustness of input conversion [34; 35; 36; 37; 38]. This study researches forest fire monitoring using the WSN, deep learning, and existing research in this field, providing critical technical support for enriching and innovating the current forest fire prevention and control technologies and management methods, considering the limitations of the WSN and deep learning-based methods. The main contributions of this study are the construction of a forest fire monitoring and early warning system based on deep learning and the internet of things (IoT). A forest fire recognition method based on image segmentation processes the collected forest fire images. Forest fire recognition is improved by combining the size, color, and shape characteristics of the flame, smoke, and area. Complex upper-layer features of fire images are efficiently extracted, improving the robustness of input conversion by building an improved dynamic convolutional neural network (DCNN) forest fire risk prediction model.