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

Abstract

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.