Abstract
Environmental monitoring systems operate under uncertainty due to incomplete observations, measurement noise, and limitations of static models. This paper proposes a model-driven framework for environmental information systems built upon the authors’ original concept of the Dynamic Intelligent Process Automation System (DIPAS). The approach integrates structured domain specifications, process-based models, and adaptive inference within a unified architecture. Domain models are treated as formal representations that are continuously transformed into executable inference mechanisms, forming digital twins that are continuously updated using streaming sensor data. DIPAS employs a Predictive Data-Adaptive Learning Mechanism (PDALM) that improves model-based estimation through adaptive, innovation-driven adjustments. The framework demonstrates how conceptual models can be operationalized within AI pipelines and used in real-time analytics. A large language model (LLM) interacts with structured model outputs, supporting model-based reasoning and decision-making. The proposed framework contributes to the development of information systems by integrating requirements, models, and AI-based implementation.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.14
Adaptive Model-Driven Inference in Intelligent Information Systems Using the DIPAS Framework for River Monitoring
Environmental monitoring systems operate under uncertainty due to incomplete observations, measurement noise, and limitations of static models. This paper proposes a model-driven framework for environmental information systems built upon the authors’ original concept of the Dynamic Intelligent Process Automation System (DIPAS). The approach integrates structured domain specifications, process-based models, and adaptive inference within a unified architecture. Domain models are treated as formal representations that are continuously transformed into executable inference mechanisms, forming digital twins that are continuously updated using streaming sensor data. DIPAS employs a Predictive Data-Adaptive Learning Mechanism (PDALM) that improves model-based estimation through adaptive, innovation-driven adjustments. The framework demonstrates how conceptual models can be operationalized within AI pipelines and used in real-time analytics. A large language model (LLM) interacts with structured model outputs, supporting model-based reasoning and decision-making. The proposed framework contributes to the development of information systems by integrating requirements, models, and AI-based implementation.
Recommended Citation
Twaróg, B., Hawro, P., Kwater, T. & Bartman, J.(2026). Adaptive Model-Driven Inference in Intelligent Information Systems Using the DIPAS Framework for River Monitoring. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.14