Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 4
Abstract
Traditional control and monitoring of water quality in drinking water distribution networks (WDNs) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identification (SysID) algorithms for generic dynamic system models seek to approximate such models using only input-output data without relying on network parameters. The objective of this paper is to investigate SysID algorithms for water quality model approximation. This research problem is challenging due to (1) complex water quality and reaction dynamics; and (2) the mismatch between the requirements of SysID algorithms and the properties of water quality dynamics. In this paper, we present the first attempt to identify water quality models in WDNs using only input-output experimental data and classical SysID methods without knowing any WDN parameters. Properties of water quality models are introduced, the ensuing challenges caused by these properties when identifying water quality models are discussed, and remedial solutions are given. Through case studies, we demonstrate the applicability of SysID algorithms, show the corresponding performance in terms of accuracy and computational time, and explore the possible factors impacting water quality model identification.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request (e.g., the tested three-node and Net1 networks and their discrete-time state-space water quality models, and the code of SIM, ERA, and OKID-ERA algorithms).
Acknowledgments
This material is based on work supported by the National Natural Science Foundation of China under Grant 62203062 and the National Science Foundation under Grant 2151392.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 7, 2021
Accepted: Nov 13, 2022
Published online: Feb 14, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 14, 2023
ASCE Technical Topics:
- Algorithms
- Business management
- Decision making
- Decision support systems
- Drinking water
- Dynamic models
- Engineering fundamentals
- Environmental engineering
- Management methods
- Mathematics
- Models (by type)
- Parameters (statistics)
- Practice and Profession
- Quality control
- Statistics
- Water (by type)
- Water and water resources
- Water management
- Water quality
- Water supply
- Water supply systems
- Water treatment
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