Wildfire Risk Assessment of Transmission-Line Corridors Based on Naïve Bayes Network and Remote Sensing Data

Chen, Weijie; Zhou, You; Zhou, Enze; Zhou, Xiang; Zhou, Wentao; Lu, Junhan

doi:10.3390/s21020634

Cited by 27 publications

(15 citation statements)

References 28 publications

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“…else if X 2 exchange � 1 then (5) exchange (BS 3 (j), BS 4 (j)) (6) else if X 3 exchange � 1 then (7) exchange (BS 1 (j), BS 3 (j)) (8) else if X 4 exchange � 1 then (9) exchange (BS 2 (j), BS 4 (j)) (10) end if (11) XFD 1 , XFD 2 , XFD 3 and XFD 4 is reshaped into square matrix XFD 1 ′ , XFD 2 ′ , XFD 3 ′ and XFD 4 ′ with size of (M/4) × (N/4), in which the size of the matrix is different from that of the first diffusion. rough the operation of equation ( 18), we will get the ciphertext matrix of each band BFC 1 , BFC 2 , BFC 3 and BFC 4 .…”

Section: Diffusion Stagementioning

confidence: 99%

“…In addition, sensor systems for acquiring remote sensing images are usually installed on satellites, spacecraft, space stations, etc. As a result, remote sensing images are exposed to space radiation environment, and the information contained in the images will inevitably face severe interference attacks and risks of being stolen during transmission [7,8]. erefore, protecting the security of remote sensing images gradually highlights its importance.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Multiband Remote-Sensing Image Encryption Algorithm Based on Dual-Channel Key Transmission Model

Liu

et al. 2021

Security and Communication Networks

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With the rapid development of remote sensing technology, satellite remote sensing images have been involved in many areas of people’s lives. Remote sensing images contain military secrets, land profiles, and other sensitive data, so it is urgent to encrypt remote sensing images. This paper proposes a dual-channel key transmission model. The plaintext related key is embedded into the ciphertext image through bit-level key hiding transmission strategy, which enhanced the ability of ciphertext image to resist known-plaintext attack and chosen plaintext attack. In addition, a multiband remote sensing image encryption algorithm based on Boolean cross-scrambling and semi-tensor product diffusion is designed. Firstly, the pixel positions of each band of the remote sensing image are disturbed. Then, the random sequence generated by the four-dimensional chaotic system is processed and deformed to obtain a Boolean matrix. Based on the generated Boolean matrix and certain rules, the cross-confusion between the bands is carried out. Finally, the semi-tensor product operation is used in the diffusion process. Simulation results and experimental analysis show that the proposed algorithm obtains a larger key space and has stronger antiattack ability than other remote sensing image encryption algorithms. It can meet the security transmission of multiband remote sensing image in open space.

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Section: Diffusion Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Multiband Remote-Sensing Image Encryption Algorithm Based on Dual-Channel Key Transmission Model

Liu

et al. 2021

Security and Communication Networks

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“…However, its complex network structure also easily leads to a reduction in computational efficiency (Dlamini, 2010). The use of Naïve Bayes (NB) based on conditional independence has the advantage of promoting computational efficiency, but neglecting the relationship between the wildfire-related factors could bring the curse of dimensionality and reduce the evaluation accuracy (Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Spatial Assessment of Wildfire Risk for Transmission-Line Corridor Based on a Weighted Naïve Bayes Model

Xiang

Zhou

et al. 2022

Front. Energy Res.

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In order to improve the wildfire prevention capacity of transmission lines, a wildfire risk assessment method for transmission-line corridors based on Weighted Naïve Bayes (WNB) is proposed in this paper. Firstly, the importance of 14 collected types of wildfire-related factors is analyzed based on the information gain ratio. Then, the optimal factors set and the most accurate sampling table are constructed by deleting the factors in the lowest order of importance one by one. Finally, the performance of the WNB model is compared to that of NB and BNW models according to the ROC curve and visualization. A total of 76.36% of fire events in 2020 fell in high-risk and very-high-risk regions, indicating the acceptable accuracy of the proposed assessment method of wildfire risk.

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“…(3) A wealth of research results exists on wildfire risk assessment and prediction models. Scholars have established statistical models, such as logistic regression [23] (LR), geographically weighted logistic regression [24](GWLR), and gompit [25]; artificial neural network models, such as a back-propagation neural network [26]; and machine learning methods, such as the random forest model [27,28], Maxent [29], and Bayesian network [4,30], through empirical analysis of case areas. These are based on datasets, such as the number of wildfire occurrences or burnt areas, and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China

Yang¹,

Jin²,

Zhou³

2021

Forests

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Forest wildfire is an important threat and disturbance facing natural forest ecosystems. Conducting wildfire risk assessments and zoning studies are of great practical significance in guiding wildfire prevention, curbing fire occurrence, and mitigating the environmental effects of wildfire. Taking Hunan Province, China as the case area, this study used remotely sensed high-temperature fire data as the wildfire sample. Twelve factors related to topography, climatic conditions, vegetation attributes, and human activities were used as environmental variables affecting wildfire occurrence. Then, a Maxent wildfire risk assessment model was constructed with GIS, which analyzed the contribution, importance, and response of environmental variables to wildfire in Hunan Province. The results show that (1) the Maxent model has high applicability and feasibility when applied to wildfire risk assessment after a test of wildfire sample sites; (2) the importance of meteorological conditions and vegetation status variables to wildfire are 54.64% and 25.40%, respectively, and their contribution to wildfire are 43.03% and 34.69%, respectively. The interaction between factors can enhance or weaken the contribution of factors on wildfire. (3) The mechanism for the effects of environmental variables on wildfire is not linear as generally believed; temperature, aridity, land use type, GDP, distance from the road, and population density have a nonlinear positive correlation with the probability of wildfire occurrence. Elevation, slope, precipitation, wind speed, and vegetation cover within the suitable interval positively contribute to the probability of wildfire, while the environmental conditions outside the suitable interval curb the occurrence of wildfire. The response of wildfire probability to forest density is U-shaped, which means either too high or too low will promote the occurrence of wildfire. (4) There is geographical variation of wildfire risk in Hunan Province. The areas at high risk and below account for 74.48% of the total area, while the areas at significantly high risk and above account for a relatively low proportion, 25.52%.

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Wildfire Risk Assessment of Transmission-Line Corridors Based on Naïve Bayes Network and Remote Sensing Data

Cited by 27 publications

References 28 publications

A Novel Multiband Remote-Sensing Image Encryption Algorithm Based on Dual-Channel Key Transmission Model

A Novel Multiband Remote-Sensing Image Encryption Algorithm Based on Dual-Channel Key Transmission Model

A Spatial Assessment of Wildfire Risk for Transmission-Line Corridor Based on a Weighted Naïve Bayes Model

Wildfire Risk Assessment and Zoning by Integrating Maxent and GIS in Hunan Province, China

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