Unsupervised anomaly detection in large-scale estuarine acoustic telemetry data

TL;DR

This paper develops and evaluates machine learning models, especially neural network autoencoders, for automated anomaly detection in large-scale estuarine acoustic telemetry data, improving detection accuracy and reducing false negatives.

Contribution

It introduces a comprehensive framework for data preprocessing, feature engineering, and model selection, with a novel threshold-finding algorithm that enhances anomaly detection performance.

Findings

NN-AE achieved high recall with no false normal classifications.

Other models had over 90% false normal rates, missing true anomalies.

NN-AE demonstrated robustness in detecting anomalies in large, complex datasets.

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.