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Fighting Wildfires——Modeling of the largest central circle of signal reception

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DOI: 10.23977/erej.2021.050104 | Downloads: 1 | Views: 67

Author(s)

Haohan Yuan 1, Luge Wang 1

Affiliation(s)

1 Hefei University of Technology, Hefei, Anhui, 230000

Corresponding Author

Haohan Yuan

ABSTRACT

Fires, especially forest fires, occur in Australia every year. The size, frequency, and loss of its scale have bothered the authorities. And now, with the development of technology, drone technology can already be used in the field of fire warning. Therefore, proper planning for drone fire protection can allow the Australian government to reduce a lot of economic losses every year. We first gridded the map of Victoria and determined the area where the UAV detection system should be deployed based on Victoria’s population distribution, fire location distribution, fire scale, fire frequency and other factors. Then, we used Monte Carlo simulation to calculate the probability that each reconnaissance UAV SSA fails to detect fires of different scales within the specified time. We control this probability to be as small as possible as one of the constraints and combine this with other constraints.

KEYWORDS

UAV, Gray forecast model, Monte Carlo simulation, Goal planning, Economical

CITE THIS PAPER

Haohan Yuan, Luge Wang, Health status assessment model of national higher education system based on BP neural network. Environment, Resource and Ecology Journal (2021) 5: 22-31. DOI: http://dx.doi.org/10.23977/erej.2021.050104

REFERENCES

[1] https://xueshu.baidu.com/usercenter/paper/show?paperid=af984f29bbfc96db20f6f034d65c77f6&site=xueshu_se
[2] http://www.bom.gov.au/weather-services/fire-weather-centre/weather-patterns/index.shtml
[3] http://www.bom.gov.au/weather-services/fire-weather-centre/bushfire-weather/index.shtml
[4] http://www.bom.gov.au/vic/forecasts/fire-danger-ratings.shtml

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