Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

Abstract

Forests are indispensable links in the ecological chain and important ecosystems in nature. The destruction of forests seriously influences the ecological environment of the Earth. Forest protection plays an important role in human sustainable development, and the most important aspect of forest protection is preventing forest fires. Fire affects the structure and dynamics of forests and also climate and geochemical cycles. Using various technologies to monitor the occurrence of forest fires, quickly finding the source of forest fires, and conducting early intervention are of great significance to reducing the damage caused by forest fires. An improved forest fire risk identification algorithm is established based on a deep learning algorithm to accurately identify forest fire risk in a complex natural environment. First, image enhancement and morphological preprocessing are performed on a forest fire risk image. Second, the suspected forest fire area is segmented. The color segmentation results are compared using the HAF and MCC methods, and the suspected forest fire area features are extracted. Finally, the forest fire risk image recognition processing is conducted. A forest fire risk dataset is constructed to compare different classification methods to predict the occurrence of forest fire risk to improve the backpropagation (BP) neural network forest fire identification algorithm. An improved machine learning algorithm is used to evaluate the classification accuracy. The results reveal that the algorithm changes the learning rate between 0.1 and 0.8, consistent with the cross-index verification of the 10x sampling algorithm. In the combined improved BP neural network and support vector machine (SVM) classifier, forest fire risk is recognized based on feature extraction and the BP network. In total, 1,450 images are used as the training set. The experimental results reveal that in image preprocessing, image enhancement technology using the frequency and spatial domain methods can enhance the useful information of the image and improve its clarity. In the image segmentation stage, MCC is used to evaluate the segmentationresults. The accuracy of this algorithm is high compared with other algorithms, up to 92.73%. Therefore, the improved forest fire risk identification algorithm can accurately identify forest fire risk in the natural environment and contribute to forest protection.

A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

“…With the rapid development of machine learning (ML), ML-based water quality assessment methods have become a hot research topic (Zheng et al, 2022). For example, Zhao, Z et al proposed a water quality assessment model based on a stochastic hybrid dynamic system (Zhao et al, 2022).…”

“…With the rapid development of machine learning (ML), ML-based water quality assessment methods have become a hot research topic ( Zheng et al., 2022 ). For example, Zhao, Z et al.…”

Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based‐BP Neural Network

“…Machine learning has been widely used in classification and prediction by scholars because it can dig out intrinsic relationships from large amounts of historical data for classification or prediction. Such as, deep learning [23,24], random forest (RF) [25], support vector machine (SVM) [26] and backpropagation neural network (BPNN) [27,28], and so on, have been gradually applied to prediction in various engineering fields because of its good accuracy. A comparative study was made by Liu et al [29] on the prediction of frost resistance of recycled concrete by using three methods, including ANN, Gaussian process regression, and multivariate adaptive regression spline; the results showed that, among the three methods, the prediction accuracy of ANN model is the best.…”

Preventing Forest Fires Using a Wireless Sensor Network

“…Fire-related loss of trees significantly increased (Figure 1). At present, methods of forest fire prevention mainly consist of patrolling, observation from watchtowers and subsequent satellite monitoring [1,2]. There are many problems with the fire protection personnel, such as inattention, absence from duty, inability to monitor in real-time and limited coverage.…”