Combination of Isolation Forest and LSTM Autoencoder for Anomaly Detection

Abstract

Land monitoring is important in agriculture. Early warning information regarding the land condition enable farmers to respond quickly when anomaly condition occures. However, identifying anomaly of land condition is not a simple task. In this research, a model of anomaly detection for land monitoring system is proposed. Raw data collected from land monitoring sensors is used as the dataset. Isolation Forest is used to transform the unlabeled data into labeled data. The labeled dataset is then used to create anomaly detection model using Long Short-Term Memory (LSTM) autoencoder. The experiments results show that the Isolation Forest has the potential to label data. The LSTM autoencoder has the accuracy 0.95 precision 0.96, recall 0.99 and flscore 0.97.