Abstract
This article presents an innovative approach to monitoring river water quality in real time by generating estimates of difficult-to-measure signals such as biochemical oxygen demand. Laboratory tests take too long for real-time monitoring. Therefore, an adaptive PDALM algorithm (Proportional Differential Algorithm with a Latch Mechanism) was developed, integrating mathematical modelling with measurement data to enable instantaneous estimation of water quality signals using a special latch mechanism. The forced eigenvalue distribution guarantees system dynamics and ensures stability and robustness to disturbances. In the proposed RTMS system, the PDALM algorithm functions as an adaptive soft sensor generating high-quality training data. This data is then used by a generative neural network for anomaly detection and forecasting of atypical scenarios in dynamic environmental systems. The system can function as an intelligent environmental monitoring module capable of learning, predicting, and responding to changing environmental conditions.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.56
Generative artificial intelligence applying an adaptive algorithm with real-time dynamics allocation for ecological monitoring
This article presents an innovative approach to monitoring river water quality in real time by generating estimates of difficult-to-measure signals such as biochemical oxygen demand. Laboratory tests take too long for real-time monitoring. Therefore, an adaptive PDALM algorithm (Proportional Differential Algorithm with a Latch Mechanism) was developed, integrating mathematical modelling with measurement data to enable instantaneous estimation of water quality signals using a special latch mechanism. The forced eigenvalue distribution guarantees system dynamics and ensures stability and robustness to disturbances. In the proposed RTMS system, the PDALM algorithm functions as an adaptive soft sensor generating high-quality training data. This data is then used by a generative neural network for anomaly detection and forecasting of atypical scenarios in dynamic environmental systems. The system can function as an intelligent environmental monitoring module capable of learning, predicting, and responding to changing environmental conditions.
Recommended Citation
Hawro, P., Kwater, T., Twarog, B. & Bartman, J. (2025). Generative artificial intelligence applying an adaptive algorithm with real-time dynamics allocation for ecological monitoringIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.56