A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors
A forest fire is a natural disaster that destroys forest resources, thus having a severe impact on humans and on the animals and plants that depend on the forest environment. This paper presents a model for predicting the scale of forest wildfires of Alberta, Canada. A fire's scale is determine...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8924693/ |
| _version_ | 1818855485490593792 |
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| author | Hao Liang Meng Zhang Hailan Wang |
| author_facet | Hao Liang Meng Zhang Hailan Wang |
| author_sort | Hao Liang |
| collection | DOAJ |
| description | A forest fire is a natural disaster that destroys forest resources, thus having a severe impact on humans and on the animals and plants that depend on the forest environment. This paper presents a model for predicting the scale of forest wildfires of Alberta, Canada. A fire's scale is determined by the combination of the fire's duration and the size of the area it burns. Our prediction model enables fire rescuers to take appropriate measures to minimize damage caused by a wildfire based on its predicted scale in the fire's early stages. The modeling data were collected from the Canada National Fire Database (CNFDB) published by Natural Resources Canada, which includes wildfire and meteorological data for Alberta, Canada. The size of the burned area and the fire's duration were used to estimate the scale of a wildfire. After multi-collinearity testing and feature normalization, the data were divided into training and testing sets. Taking the meteorological factors as input values, a backpropagation neural network (BPNN), a recurrent neural network (RNN), and long short-term memory (LSTM) were implemented to establish prediction models. Of these classification methods, LSTM exhibited the highest accuracy, 90.9%. The results indicate that it is feasible to predict the scale of a forest wildfire at the beginning of its occurrence using meteorological information. |
| first_indexed | 2024-12-19T08:09:21Z |
| format | Article |
| id | doaj.art-ba48f53660ad4a7087369c903b4dd719 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:09:21Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ba48f53660ad4a7087369c903b4dd7192022-12-21T20:29:40ZengIEEEIEEE Access2169-35362019-01-01717674617675510.1109/ACCESS.2019.29578378924693A Neural Network Model for Wildfire Scale Prediction Using Meteorological FactorsHao Liang0https://orcid.org/0000-0002-9540-4627Meng Zhang1https://orcid.org/0000-0002-2673-8391Hailan Wang2https://orcid.org/0000-0002-4740-4606School of Technology, Beijing Forestry University, Beijing, ChinaSchool of Technology, Beijing Forestry University, Beijing, ChinaSchool of Technology, Beijing Forestry University, Beijing, ChinaA forest fire is a natural disaster that destroys forest resources, thus having a severe impact on humans and on the animals and plants that depend on the forest environment. This paper presents a model for predicting the scale of forest wildfires of Alberta, Canada. A fire's scale is determined by the combination of the fire's duration and the size of the area it burns. Our prediction model enables fire rescuers to take appropriate measures to minimize damage caused by a wildfire based on its predicted scale in the fire's early stages. The modeling data were collected from the Canada National Fire Database (CNFDB) published by Natural Resources Canada, which includes wildfire and meteorological data for Alberta, Canada. The size of the burned area and the fire's duration were used to estimate the scale of a wildfire. After multi-collinearity testing and feature normalization, the data were divided into training and testing sets. Taking the meteorological factors as input values, a backpropagation neural network (BPNN), a recurrent neural network (RNN), and long short-term memory (LSTM) were implemented to establish prediction models. Of these classification methods, LSTM exhibited the highest accuracy, 90.9%. The results indicate that it is feasible to predict the scale of a forest wildfire at the beginning of its occurrence using meteorological information.https://ieeexplore.ieee.org/document/8924693/Forest wildfireLSTM modelmeteorological factorsprediction of wildfire scale |
| spellingShingle | Hao Liang Meng Zhang Hailan Wang A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors IEEE Access Forest wildfire LSTM model meteorological factors prediction of wildfire scale |
| title | A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors |
| title_full | A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors |
| title_fullStr | A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors |
| title_full_unstemmed | A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors |
| title_short | A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors |
| title_sort | neural network model for wildfire scale prediction using meteorological factors |
| topic | Forest wildfire LSTM model meteorological factors prediction of wildfire scale |
| url | https://ieeexplore.ieee.org/document/8924693/ |
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