Predicting the rate of forest fire spread toward any directions based on a CNN model considering the correlations of input variables-Reference-Cited by-同舟云学术

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Predicting the rate of forest fire spread toward any directions based on a CNN model considering the correlations of input variables

Published:2022-10-25 Issue:2 Volume:28 Page:111-119
ISSN:1341-6979
Container-title:Journal of Forest Research
language:en
Short-container-title:Journal of Forest Research

Author:

Li Xingdong¹²^ORCID,Lin Chuanying¹^ORCID,Zhang Mingxian¹,Li Sanping¹,Sun Shufa²³,Liu Jiuqing¹,Hu Tongxin²⁴,Sun Long²⁴

Affiliation:

1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China

2. Northern Forest Fire Management Key Laboratory of the State Forestry and Grassland Bureau, Northeast Forestry University, Harbin, China

3. College of Engineering and Technology, Northeast Forestry University, Harbin, China

4. College of Forestry, Northeast Forestry University, Harbin, China

Funder

Natural Science Foundation of Heilongjiang Province of China

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Forestry

Link

https://www.tandfonline.com/doi/pdf/10.1080/13416979.2022.2138096

Reference27 articles.

1. Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

2. Connections of climate change and variability to large and extreme forest fires in southeast Australia

3. Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient

4. The Rothermel surface fire spread model and associated developments: A comprehensive explanation

5. Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire

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1. Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method;Journal of Environmental Informatics;2024

2. Predicting the Continuous Spatiotemporal State of Ground Fire Based on the Expended LSTM Model with Self-Attention Mechanisms;Fire;2023-06-15

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