Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning

Abstract

The use of bivalve mollusks as bioindicators in automated monitoring systems can provide real-time detection of emergency situations associated with the pollution of aquatic environments. The behavioral reactions of Unio pictorum (Linnaeus, 1758) were employed in the development of a comprehensive automated monitoring system for aquatic environments by the authors. The study used experimental data obtained by an automated system from the Chernaya River in the Sevastopol region of the Crimean Peninsula. Four traditional unsupervised machine learning techniques were implemented to detect emergency signals in the activity of bivalves: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier factor (LOF). The results showed that the use of the elliptic envelope, iForest, and LOF methods with proper hyperparameter tuning can detect anomalies in mollusk activity data without false alarms, with an F1 score of 1. A comparison of anomaly detection times revealed that the iForest method is the most efficient. These findings demonstrate the potential of using bivalve mollusks as bioindicators in automated monitoring systems for the early detection of pollution in aquatic environments.

Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms

“…Computation time for the SARIMA model, with settings of averaging time 5 min and 6 forecasting points (the best detection time for the second anomaly by Theta, Croston, and Prophet methods), was 1 h and 40 min. Comparison with estimates presented in [20], which describes the possibility of detecting anomalies in the same bivalves' data using four unsupervised machine learning algorithms: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier level (LOF), showed using the example of the first anomaly (anomaly 3 in [20]) that the use of machine learning algorithms lost by almost an hour in the speed of anomaly detection, compared to the Prophet method (Table 6). Thus, the algorithms discussed in the article turned out to be better in terms of anomaly detection time and faster in computational complexity, compared to the SARIMA model.…”

“…In a previous work, we investigated the ability to detect anomalies in bivalves' activity data using four unsupervised machine learning algorithms (elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier level (LOF) [20], the autoregressive integrated moving average (ARIMA) forecast model with a seasonal component in the same data [21], obtained by biological early warning system [22]). In this paper, we further analyzed machine learning forecasting algorithms for identifying anomalies and generating alarms in bivalves' activity data.…”

Synthetic Generation and Detection of Crown-of-Thorns Starfish Instances retrival in Underwater Image

“…Achieves industrial precision and speed, demonstrating deep learning's potential for quality control [16].Enhanced PP-YOLO and improved Deep-SORT, with ResNet50 and margin loss, develop a real-time UAV tracking system (91.6%) that overcomes minor, featureless problems and interference issues [17].Traditional fire detection approaches have drawbacks; PP-YOLO, a cutting-edge object recognition model, uses computer vision and deep learning to enhance, promising early fire detection [18]. Study pioneers' real-time bivalve behavior monitoring for pollution utilizing four machine learning approaches, achieving anomaly identification without false alarms, demonstrating the possibility for automated aquatic pollution monitoring [19]. This paper describes an optimized autoencoder for real-time water quality anomaly detection, emphasizing clean data patterns to improve pollutant discrimination.…”

Freshwater Mussels as Sentinels for Safe Drinking Water Supply in Europe

“…More recently, however, the behavioral response (e.g., swim speed, distance moved, activity levels) of different organismsincluding waterfleas, mussels, and fisheshas been widely implemented, especially in Europe and Asia . In addition, the technological development of data acquisition systems allowed incorporation of automated monitoring and machine learning in unsupervised BEWSs …”

“… 8 In addition, the technological development of data acquisition systems allowed incorporation of automated monitoring and machine learning in unsupervised BEWSs. 9 …”