Predicting the Wildland Fire Spread Using a Mixed-Input CNN Model with Both Channel and Spatial Attention Mechanisms

Li, Xingdong; Wang, Xinyu; Sun, Shuliang; Wang, Yangwei; Li, Sanping; Li, Dandan

doi:10.1007/s10694-023-01427-2

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…The spatial attention mechanism allows for weighting between the height and width dimensions of the feature map to improve the representation of features [29]. In this module, the input feature map is first compressed in the spatial direction to obtain the mean and maximum values for each spatial location.…”

Section: Attention Mechanismmentioning

confidence: 99%

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Zhang,

Sun,

et al. 2024

Applied Sciences

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Aiming at the influence of the fundamental frequency and its harmonics in transformer vibration signals on fault signals, which cause a low fault identification rate and degradation of classification model performance, a new strategy is proposed for fault diagnosis using periodic map spectrum feature maps as input features. In this study, the optimal decomposition parameters were first found adaptively using the VMD improved by the positive cosine optimisation algorithm; then, the transformer vibration signal was modally decomposed, and the periodic map spectrum features were plotted according to the differences in the energy distribution of the different modal components at different frequencies. Finally, the GoogLeNet classification model with the improved attention mechanism assigned different weights to the feature maps in both spatial and channel dimensions to improve the classification accuracy and achieve transformer fault diagnosis. The experimental results verified the validity of the adopted feature map and the proposed model, and the accuracy was significantly improved to 99.04% compared with the traditional GoogLeNet, which is valuable for engineering applications.

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Section: Attention Mechanismmentioning

confidence: 99%

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Zhang,

Sun,

et al. 2024

Applied Sciences

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“…Because of the ability of the meta-cellular automata model to simulate the evolutionary process of complex systems, it is widely used in forest fire spread simulations [30][31][32]. With the in-depth study of machine learning, the algorithms of random forests, neural networks, and support vector machines have performed well in forest fire spread simulations [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

Ning,

Liu,

et al. 2024

Forests

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With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

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Distributed Random Forest for Predicting Forest Wildfires Based on Weather Data

Damaŝeviŝius,

Maskeliũnas

2024

Communications in Computer and Information Science

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Predicting the Wildland Fire Spread Using a Mixed-Input CNN Model with Both Channel and Spatial Attention Mechanisms

Cited by 4 publications

References 43 publications

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

Distributed Random Forest for Predicting Forest Wildfires Based on Weather Data

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