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Suppressing Forest Fires in Global Climate Change Through Artificial Intelligence: A Case Study on British Columbia | IEEE Conference Publication | IEEE Xplore

Suppressing Forest Fires in Global Climate Change Through Artificial Intelligence: A Case Study on British Columbia


Abstract:

Forest fires used to be a tool for human survival. In primitive society, humans could use forest fires to complete hunting and land reclamation work. In modern society, f...Show More

Abstract:

Forest fires used to be a tool for human survival. In primitive society, humans could use forest fires to complete hunting and land reclamation work. In modern society, forest fires pose a massive threat to the environment. This paper analyzes a forest fire case in British Columbia. Firstly, this paper summarizes the most controversial factors that cause forest fires, such as global warming, changes in local precipitation, and bark beetles. Secondly, this paper explores the impact of forest fires, the losses to the logging industry, such as the recovery costs of forests, the reduction of carbon sinks against climate change and the influence on human health. The conclusion of this paper through case study is: firstly, global warming and changes in local precipitation and other factors will provide conditions for the spread of forest fires, increasing the number of wildfires in the forest. Secondly, bark beetles will produce more fuel. They also reduce the availability of forest logs and limit the development of logging. Thirdly, uncontrollable forest fires will destroy forests and nearby infrastructure and increase recovery costs. Fourthly, reducing forest areas will destroy carbon sinks. Fifthly, the particles produced by forest fires will cause severe heart and lung diseases in the human body. Finally, this paper summarizes four solutions to forest fires. Forest Fire Suppression Through Artificial intelligence, Bark Beetle Removal Solution and Climate Change Technological Solution. In the Artificial Intelligence solution part, Support vector machine (SVM) and random forest were used to analyze four groups of different characteristics, distribution space, time, climate index and FWI system index. By inputting four basic Meteorologies, such as temperature, relative humidity, wind speed and precipitation, the model can accurately predict the affected area of small-scale and frequent fires.
Date of Conference: 20-22 January 2022
Date Added to IEEE Xplore: 20 April 2022
ISBN Information:
Conference Location: Sanya, China

References

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