Scientists Come Up With New Way of Predicting Forest Fires

08 June 2020

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RUSSIA – Scientists from the Tomsk Polytechnic University (TPU) have proposed a new mathematical classification model for forest combustible materials to effectively forecast forest fires, the university’s press service said.

Forest fires are a powerful natural and anthropogenic factor that significantly affects forest ecosystems. For countries where forests occupy large territories, fires are a national problem and cause great damage to the economy.

The classification of forest combustibles helps predict the occurrence of forest fires. According to the researchers, currently, all such classification systems are only descriptive and have nothing to do with the physics behind the ignition of forest combustibles.

“We have developed a mathematical model for physically justified forest combustibles’ classification based on the relationship between the forest combustible materials flammability indices and their properties. Our model will help classify any forest combustible materials according to their physical properties, rather than using descriptive characteristics”, Nikolai Baranovsky, associate professor at TPU’s Butakov Research Centre, said.

According to the scientist, this will allow for the creation of a unified forest combustible materials classification system for all those territories covered by forests, and make it possible to create a unified system to forecast forest fire risks.

© AP Photo / Rajesh Kumar Singh

“We believe that the prevention of forest fires is the most effective measure to combat them since extinguishing them is either impossible or inefficient”, Baranovsky stressed.

The scientists have also developed a mathematical model of heat transfer in a grass stem, which will minimise the risk of fire and help better understand what happens at a fire’s initial stages. The study results were published in the journal Safety.

The study was supported by the Russian Foundation for Basic Research as part of the scientific project 17-29-05093.

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