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

SUBMITTER: Grekov AN 

PROVIDER: S-EPMC10007031 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature