Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China

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 probabiliti...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zechuan Wu [verfasserIn] Mingze Li [verfasserIn] Bin Wang [verfasserIn] Ying Quan [verfasserIn] Jianyang Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	forest management Heilongjiang forest area fire prediction ANN

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 13(2021), 9, p 1813
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:9, p 1813

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs13091813

Katalog-ID:	DOAJ019435541

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author	Zechuan Wu
spellingShingle	Zechuan Wu misc forest management misc Heilongjiang forest area misc fire prediction misc ANN misc Science misc Q Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China
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topic_title	Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China forest management Heilongjiang forest area fire prediction ANN
topic	misc forest management misc Heilongjiang forest area misc fire prediction misc ANN misc Science misc Q
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title	Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China
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title_full	Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China
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title_sort	using artificial intelligence to estimate the probability of forest fires in heilongjiang, northeast china
title_auth	Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China
url	https://doi.org/10.3390/rs13091813 https://doaj.org/article/2f1a2f0a765c44d8a1073bb743ef165b https://www.mdpi.com/2072-4292/13/9/1813 https://doaj.org/toc/2072-4292
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up_date	2024-07-03T23:29:50.610Z
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Using Artificial Intelligence to Estimate the Probability of Forest Fires in Heilongjiang, Northeast China

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