Abstract
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.
Highlights
Forest fire is one of the major natural disasters, and it occurred frequently in the last few years [1]
The design of progressive neural unit (CSG-Long Short-Term Memory (LSTM)) is as Figure 4: The forget gate control function is given by the accessory neural unit, so that the model can sense the change of external wind speed in real time, and accelerate the rate of learning the forest fire spreading speed after the main neural unit adapts the change of wind speed
Under this structure it is considered that there is a strong interaction between the wind speed and the forest fire spread rate, in other words, the change of the wind speed will cause the change of the forest fire spread rate, and at the same time, the local wind speed of the fire site will be affected by the feedback of the flame
Summary
Forest fire is one of the major natural disasters, and it occurred frequently in the last few years [1]. Based on physics and statistical experience, some classic forest fire models such as Albini model [4], Australian Mcarthur model [5], Canadian forest fire model [6], Rothermel model [7,8] and Wang Zhengfei model [9] are proposed These theoretical models fully demonstrate the relationship between the spread of forest fires and the characteristics of combustibles and environmental factors on the basis of a large number of forest fire experiments, and quantify their use of mathematical relationships to reflect their mutual effects. In order to make the LSTM neural network be able to perceive the changes of the external environment while learning the fire spread rate, we introduced the progressive structure into the network unit to make the model have good real time performance.
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