LSTM-Based Anomaly Detection Framework for Intelligent Forest Monitoring
Author
A Saisree, Sesham. Prathibha, Konda Nithyasri, Peddakondrolla Poojithareddy
Abstract
Forest ecosystems are vulnerable to anomalous events such as forest fires, illegal activities, abnormal vegetation changes, pest infestations, and sudden environmental variations. Early detection of such anomalies is essential for effective forest monitoring and rapid response. This study proposes an LSTM-based anomaly detection framework for intelligent forest monitoring using time-series environmental and sensor observations. The proposed model analyzes sequential patterns in parameters such as temperature, humidity, smoke concentration, rainfall, soil moisture, and atmospheric conditions to distinguish normal forest conditions from anomalous events. The LSTM network learns temporal dependencies among successive observations and identifies deviations from learned normal behavioral patterns using reconstruction/prediction error. The system was evaluated using a representative forest-monitoring dataset containing 50,000 time-series observations, with 80% used for training and 20% for testing. Experimental results demonstrate that the proposed LSTM model achieved an accuracy of 97.42%, precision of 96.85%, recall of 95.73%, F1-score of 96.29%, and AUC of 98.16%. The model obtained an anomaly detection rate of 95.73% while maintaining a relatively low false-alarm rate of 2.58%. The results indicate that LSTM effectively captures temporal variations in forest environmental conditions and can detect abnormal patterns before they develop into potentially severe events. The proposed approach can be integrated with IoT sensor networks, wireless communication systems, and real-time forest surveillance platforms to provide continuous and automated monitoring. The system therefore offers a practical machine-learning-based solution for early anomaly identification, supporting forest conservation, environmental protection, and rapid emergency response.
Keywords
LSTM, Forest Anomaly Detection, Time-Series Analysis, Environmental Monitoring, IoT Sensor Networks, Deep Learning.
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References
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