Automated Early Warning System for Water Environment Based on Behavioral Reactions of Bivalves

Grekov, A.N.; Kuzmin, K.A.; Мишуров, В. Ж.

doi:10.1109/rusautocon.2019.8867621

Cited by 4 publications

(2 citation statements)

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“…Measured activity data from the freshwater bivalve Unio pictorum (Linnaeus, 1758) collected between March and April 2017 were used to create forecasting models. The data were obtained using the BEWS developed by the authors, based on recording the behavioral reactions of bivalve mollusks [22]. The BEWS consisted of surface and underwater parts (Figure 1).…”

Section: Datamentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Grekov,

Vyshkvarkova,

Mavrin

2024

Forecasting

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Evaluation of water quality and accurate prediction of water pollution indicators are key components in water resource management and water pollution control. The use of biological early warning systems (BEWS), in which living organisms are used as biosensors, allows for a comprehensive assessment of the aquatic environment state and a timely response in the event of an emergency. In this paper, we examine three machine learning algorithms (Theta, Croston and Prophet) to forecast bivalves’ activity data obtained from the BEWS developed by the authors. An algorithm for anomalies detection in bivalves’ activity data was developed. Our results showed that for one of the anomalies, Prophet was the best method, and for the other two, the anomaly detection time did not differ between the methods. A comparison of methods in terms of computational speed showed the advantage of the Croston method. This anomaly detection algorithm can be effectively incorporated into the software of biological early warning systems, facilitating rapid responses to changes in the aquatic environment.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%