基于多变量LSTM神经网络的澳大利亚大火预测研究

长短周期记忆神经网络(LSTM)受益于能够捕获长期依赖关系的特点，在许多实际应用中展现了优异的性能。该文构建了LSTM多变量数据驱动的预测模型，通过多变量输入的方式预测澳大利亚森林大火。首先使用多变量LSTM预测模型对日最高温度进行预测，并与反向传播(BP)神经网络以及ARIMA预测模型的结果进行对比。研究表明:以相关变量为输入的BP神经网络无法考虑时序变化规律，预测误差最大；以温度单变量为输入的ARIMA根据时序变化做出相应预测，预测效果较好；多变量LSTM预测模型综合考虑了多种因素的相互影响，同时结合了时间序列依赖关系，预测效果最好。最后通过多变量LSTM预测模型对某节点是否着火进行了预测，预测结果与实际值契合较好。总体来说，多变量LSTM预测模型对澳大利亚大火的预测结果可信。

Multivariable LSTM Neural Network Model for Australia Fire Prediction

Long Short-Term Memory (LSTM) neural network benefits from its ability to capture long-term dependencies and has shown excellent performance in many practical applications. Multivariable LSTM data-driven prediction model is constructed in this paper to predict Australian forest fires by multivariable input. Firstly, the multivariate LSTM prediction model is used to predict the maximum daily temperature, and the results are compared with those of the back-propagation (BP) neural network and Autoregressive Integrated Moving Average model (ARIMA) prediction model. The results show that the BP neural network with the related variables as input cannot consider the time-series variation law, and the prediction error is the largest. ARIMA with single temperature as input makes corresponding prediction according to time series change, and the prediction effect is good. Multivariable LSTM prediction model comprehensively considers the interaction of many factors, and combines the time series dependence, the prediction effect is the best. Finally, the multivariable LSTM prediction model is used to predict whether a node is on fire, and the prediction results are in good agreement with the actual value. Overall, the multivariable LSTM prediction model is reliable in predicting the Australian fires.