用于野火预测中无监督异常检测的深度自编码器
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
AI总结：
本研究提出基于深度自编码器的两种无监督异常检测方法用于野火预测，利用澳大利亚2005-2021年气象数据，发现全连接自编码器在无标签情况下表现最佳，准确率达0.71。
摘要：
由于气候危机，野火对全球生态系统构成的危害正显著增加。鉴于其复杂的性质，迫切需要采用机器学习等创新方法进行野火预测。本研究采用了一种独特的方法，不同于经典的监督学习，通过使用自编码器和聚类技术进行异常检测，填补了无监督野火预测的空白。研究使用了澳大利亚2005年至2021年的历史天气和归一化植被指数数据集。研究分析了两种主要的无监督方法。第一种使用深度自编码器获取潜在特征，然后将其输入到聚类模型、孤立森林、局部异常因子和单类SVM中进行异常检测。第二种方法使用深度自编码器重建输入数据，并利用重建误差来识别异常。在这部分中采用了长短期记忆（LSTM）自编码器和全连接（FC）自编码器，两者均以无监督方式仅从标称数据中学习。FC自编码器表现优于同类方法，实现了0.71的准确率、0.74的F1分数和0.42的MCC。这些发现突显了该方法的实用性，因为它利用无监督学习技术，在缺乏真实标签的情况下能够有效预测野火。
英文摘要：
Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a unique approach, differentiating from classical supervised learning, and addressed the gap in unsupervised wildfire prediction using autoencoders and clustering techniques for anomaly detection. Historical weather and normalised difference vegetation index datasets of Australia for 2005 - 2021 were utilised. Two main unsupervised approaches were analysed. The first used a deep autoencoder to obtain latent features, which were then fed into clustering models, isolation forest, local outlier factor and one-class SVM for anomaly detection. The second approach used a deep autoencoder to reconstruct the input data and use reconstruction errors to identify anomalies. Long Short-Term Memory (LSTM) autoencoders and fully connected (FC) autoencoders were employed in this part, both in an unsupervised way learning only from nominal data. The FC autoencoder outperformed its counterparts, achieving an accuracy of 0.71, an F1-score of 0.74, and an MCC of 0.42. These findings highlight the practicality of this method, as it effectively predicts wildfires in the absence of ground truth, utilising an unsupervised learning technique.
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