Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind

Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the pro...

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Autores principales:	Xingdong Li, Hewei Gao, Mingxian Zhang, Shiyu Zhang, Zhiming Gao, Jiuqing Liu, Shufa Sun, Tongxin Hu, Long Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	UAV remote sensing forest fire fire spread modelling LSTM wind prediction Science Q
Acceso en línea:	https://doaj.org/article/797219ca3a504f9782a0df5107c41d23
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spelling	oai:doaj.org-article:797219ca3a504f9782a0df5107c41d232021-11-11T18:53:54ZPrediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind10.3390/rs132143252072-4292https://doaj.org/article/797219ca3a504f9782a0df5107c41d232021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4325https://doaj.org/toc/2072-4292Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires.Xingdong LiHewei GaoMingxian ZhangShiyu ZhangZhiming GaoJiuqing LiuShufa SunTongxin HuLong SunMDPI AGarticleUAV remote sensingforest firefire spread modellingLSTMwind predictionScienceQENRemote Sensing, Vol 13, Iss 4325, p 4325 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	UAV remote sensing forest fire fire spread modelling LSTM wind prediction Science Q
spellingShingle	UAV remote sensing forest fire fire spread modelling LSTM wind prediction Science Q Xingdong Li Hewei Gao Mingxian Zhang Shiyu Zhang Zhiming Gao Jiuqing Liu Shufa Sun Tongxin Hu Long Sun Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind
description	Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires.
format	article
author	Xingdong Li Hewei Gao Mingxian Zhang Shiyu Zhang Zhiming Gao Jiuqing Liu Shufa Sun Tongxin Hu Long Sun
author_facet	Xingdong Li Hewei Gao Mingxian Zhang Shiyu Zhang Zhiming Gao Jiuqing Liu Shufa Sun Tongxin Hu Long Sun
author_sort	Xingdong Li
title	Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind
title_short	Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind
title_full	Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind
title_fullStr	Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind
title_full_unstemmed	Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind
title_sort	prediction of forest fire spread rate using uav images and an lstm model considering the interaction between fire and wind
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/797219ca3a504f9782a0df5107c41d23
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Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind

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