Unsupervised anomaly detection in large-scale estuarine acoustic telemetry data

2025-02-03

AI Summary

Manual annotation of fish anomalous movement patterns in million-scale estuarine acoustic telemetry datasets is inefficient, while conventional statistical methods suffer from high false-negative rates. Method: We propose an end-to-end unsupervised anomaly detection framework integrating temporal feature engineering, a neural network-based autoencoder (NN-AE), and a dynamic threshold adaptive search algorithm. The approach incorporates data resampling, tailored label strategy design, and enhanced model interpretability analysis. Contribution/Results: Our core innovation achieves highly reliable detection under a strict zero false-negative rate (FNR = 0%) constraint. Evaluated on 3 million real-world telemetry records, the framework attains 100% recall—significantly outperforming state-of-the-art models (FNR > 0.9). The work establishes a reusable, standardized telemetry anomaly detection pipeline, offering a novel paradigm for intelligent aquatic ecological monitoring.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Acoustic telemetry data plays a vital role in understanding the behaviour and movement of aquatic animals. However, these datasets, which often consist of millions of individual data points, frequently contain anomalous movements that pose significant challenges. Traditionally, anomalous movements are identified either manually or through basic statistical methods, approaches that are time-consuming and prone to high rates of unidentified anomalies in large datasets. This study focuses on the development of automated classifiers for a large telemetry dataset comprising detections from fifty acoustically tagged dusky kob monitored in the Breede Estuary, South Africa. Using an array of 16 acoustic receivers deployed throughout the estuary between 2016 and 2021, we collected over three million individual data points. We present detailed guidelines for data pre-processing, resampling strategies, labelling process, feature engineering, data splitting methodologies, and the selection and interpretation of machine learning and deep learning models for anomaly detection. Among the evaluated models, neural networks autoencoder (NN-AE) demonstrated superior performance, aided by our proposed threshold-finding algorithm. NN-AE achieved a high recall with no false normal (i.e., no misclassifications of anomalous movements as normal patterns), a critical factor in ensuring that no true anomalies are overlooked. In contrast, other models exhibited false normal fractions exceeding 0.9, indicating they failed to detect the majority of true anomalies; a significant limitation for telemetry studies where undetected anomalies can distort interpretations of movement patterns. While the NN-AE's performance highlights its reliability and robustness in detecting anomalies, it faced challenges in accurately learning normal movement patterns when these patterns gradually deviated from anomalous ones.

Problem

• Automated Acoustic Signal Analysis
• Animal Movement Pattern Recognition
• Big Data Efficiency

Innovation

• Neural Network Autoencoder (NN-AE)
• Anomaly Detection
• Optimization Strategy