A Brief Review of Machine Learning Algorithms in Forest Fires Science

Alkhatib, Ramez; Sahwan, Wahib; Anas, Alkhatieb,; Schütt, Brigitta

doi:10.3390/app13148275

Cited by 28 publications

(26 citation statements)

References 107 publications

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Section: Yolo Series Algorithms Comparisonmentioning

confidence: 99%

“…The algorithm has significantly improved from the first version to the latest one. In order to compare the detection performance of different YOLO series on this dataset, we present the detection results in Table 2, and the mAP 50 and Recall results in Figure 7. From Table 2, it can be clearly seen that YOLOv5 achieved a mAP 50 of 77.8%, be er than other YOLO versions in terms of detection precision.…”

Section: Yolo Series Algorithms Comparisonmentioning

confidence: 99%

“…The experimental results are summarized in Table 3, and Figure 8 shows the loss curves of these algorithms in the ablation process. From Table 3, it is evident that the inclusion of the SAC module significantly enhances mAP 50 and reduces the computational complexity of the algorithm. This indicates that, while improving mAP 50 , the overall complexity of the algorithm decreases.…”

Section: Analysis Of Ablation Experimentsmentioning

confidence: 99%

“…From Table 3, it is evident that the inclusion of the SAC module significantly enhances mAP 50 and reduces the computational complexity of the algorithm. This indicates that, while improving mAP 50 , the overall complexity of the algorithm decreases. Furthermore, From Table 3, it is evident that the inclusion of the SAC module significantly enhances mAP 50 and reduces the computational complexity of the algorithm.…”

Section: Analysis Of Ablation Experimentsmentioning

confidence: 99%

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An Efficient Forest Fire Detection Algorithm Using Improved YOLOv5

Shi,

Lu,

Wang

et al. 2023

Forests

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Forest fires result in severe disaster, causing significant ecological damage and substantial economic losses. Flames and smoke represent the predominant characteristics of forest fires. However, these flames and smoke often exhibit irregular shapes, rendering them susceptible to erroneous positive or negative identifications, consequently compromising the overall performance of detection systems. To enhance the average precision and recall rates of detection, this paper introduces an enhanced iteration of the You Only Look Once version 5 (YOLOv5) algorithm. This advanced algorithm aims to achieve more effective fire detection. First, we use Switchable Atrous Convolution (SAC) in the backbone network of the traditional YOLOv5 to enhance the capture of a larger receptive field. Then, we introduce Polarized Self-Attention (PSA) to improve the modeling of long-range dependencies. Finally, we incorporate Soft Non-Maximum Suppression (Soft-NMS) to address issues related to missed detections and repeated detections of flames and smoke by the algorithm. Among the plethora of models explored, our proposed algorithm achieves a 2.0% improvement in mean Average Precision@0.5 (mAP50) and a 3.1% enhancement in Recall when compared with the YOLOv5 algorithm. The integration of SAC, PSA, and Soft-NMS significantly enhances the precision and efficiency of the detection algorithm. Moreover, the comprehensive algorithm proposed here can identify and detect key changes in various monitoring scenarios.

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Section: Yolo Series Algorithms Comparisonmentioning

confidence: 99%

Section: Yolo Series Algorithms Comparisonmentioning

confidence: 99%

Section: Analysis Of Ablation Experimentsmentioning

confidence: 99%

Section: Analysis Of Ablation Experimentsmentioning

confidence: 99%

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An Efficient Forest Fire Detection Algorithm Using Improved YOLOv5

Shi,

Lu,

Wang

et al. 2023

Forests

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“…Additionally, a comparison study highlights the advantages and disadvantages of the most critical aspects of the present approaches, such as detection accuracy. Alkhatib R et al[2], 2023, explores the application of various machine learning algorithms for forest re detection and prediction. It discusses the advantages and limitations of algorithms such as decision trees, random forests, support vector machines, k-nearest neighbours, and neural networks.…”

mentioning

confidence: 99%

A Study of Engine Culture towards Dropping the Probability of Jungle Blazes

Singh

2023

Preprint

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Forest fire prevention is critical to protecting our natural resources and ensuring the safety of wildlife and communities. Traditional fire prevention methods have limitations in detecting and predicting fires before they cause irreparable damage. This report proposes a novel approach using machine learning to enhance forest fire prevention efforts. By leveraging machine learning algorithms and analyzing relevant data such as oxygen levels, temperature, and humidity, we can develop a predictive model to assess the probability of a fire occurring in a particular area. This model can be a valuable tool for governments and authorities to allocate resources effectively and proactively to prevent fires. The research focuses on creating a large dataset by collecting real-life examples of forest fires and their associated parameters. With this dataset, a machine learning model can be trained to accurately predict the likelihood of a fire outbreak based on the input parameters. Integrating this model into a web or mobile application can provide users with real-time fire risk assessments and alerts, enabling them to take necessary precautions. The proposed system offers a comprehensive and efficient solution for forest fire prevention, with the potential to be applied globally. By combining machine learning capabilities with preventive measures and community involvement, we can significantly reduce the negative impact of forest fires and safeguard the natural environment.

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