Document


Title

Using artificial intelligence to estimate the probability of forest fires in Heilongjiang, northeast China
Document Type: Journal Article
Author(s): Zechuan Wu; Mingze Li; Bin Wang; Ying Quan; Jianyang Liu
Publication Year: 2021

Cataloging Information

Keyword(s):
  • ANN - artificial neural networks
  • China
  • fire prediction
  • fire probability
  • forest area
  • forest management
  • Heilongjiang province
Region(s):
  • International
Record Maintained By:
Record Last Modified: June 9, 2021
FRAMES Record Number: 63788

Description

Although low-intensity forest fires are a necessary part of healthy echo system, high-intensity forest fires continue to affect the diversity of forest ecosystems and species. Therefore, it is necessary to study the driving factors of forest fires and explore their possible locations and probabilities in complex forest terrain. In this article, we determined the relative influences of different types of factors on the occurrence of forest fires in Heilongjiang forest areas and compared the performance of artificial neural networks and logistic regression for wildfire prediction. By analyzing Heilongjiang forest fire data from 2002 to 2015 and constructing a model, we found that climate factors, topographical factors, and vegetation type factors play a crucial role in Heilongjiang’s wildfires. During the fire prevention period, temperature and wind speed have a more significant influence than other factors. According to the variable screening that we conducted, the model built by the variables that we used can predict 84% of forest fires in Heilongjiang Province. For recent wildfires (2019–2020) in most areas, we can use artificial neural networks for relatively accurate verification (85.2%). Therefore, artificial neural networks are very suitable for the prediction of forest fires in Heilongjiang Province. Through the prediction results, we also created a probability distribution map of fire occurrence in the study area. On this basis, we also analyzed the changes in the probability of natural fires under the weather changing trend, which can effectively aid in fire prevention and extinguishment.

Online Link(s):
Citation:
Wu, Zechuan; Li, Mingze; Wang, Bin; Quan, Ying; Liu, Jianyang. 2021. Using artificial intelligence to estimate the probability of forest fires in Heilongjiang, northeast China. Remote Sensing 13(9):1813.