Prediction model of water content in surface dead fuel based on convolution neural network and meteoro-logical factors regression.

Water content of surface fuels is an important indicator of forest fire risk level and fire behavior, and the prediction model of which has a significant effect on fire risk prediction and management. Based on field meteorological factors of and var. forests and water content data of dead fuels on the ground, we conducted the relative importance ranking of meteorological factors random forest and Pearson correlation analysis, and predicted water content of fuels using deeply learned convolutional neural network and meteorological factors regression.

Prediction models of fire spread rate of plantation's surface fuel.

plantation has high fire risks due to high oil content in branches and leaves. The spread of surface fire is the main way of forest fire expansion. Understanding the surface fire spread rate can provide scientific guidance for fire fighting.

[Pyrolysis and gas emissions characteristics of six tree species in Heilongjiang Province, China].

Forest fuels are the basis of fire occurrences, while ground dead fuels are an important part of forest fuels. Undestanding the pyrolysis characteristics and gas emissions of forest fuels is of great significance to explore the effects of forest fire on atmospheric environment and carbon balance, as well as to prevent and combat forest fire. In this study, the thermogravimetric analysis and gas emission analysis were conducted on leaf litter of six tree species ( var.