Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36379
Title: A database system for querying of river networks: facilitating monitoring and prediction applications
Authors: BOLLEN, Erik 
Pagan, Brianna R.
KUIJPERS, Bart 
Van Hoey, Stijn
Desmet, Nele
Hendrix , Rik
Dams, Jef
Seuntjens, Piet
Issue Date: 2022
Publisher: IWA PUBLISHING
Source: Water supply, 22 (3) , p. 2832-2846
Abstract: The increasing availability of real-time in situ measurements and remote sensing observations have the potential to contribute to the optimization of water resources management. Global challenges such as climate change, intensive agriculture and urbanization put a high pressure on our water resources. Due to recent innovations in measuring both water quantity and quality, river systems can now be monitored in real time at an unprecedented spatial and temporal scale. To interpret the sensor measurements and remote sensing observations additional data for example on: the location of the measurement, upstream and downstream catchment characteristics, horizontal ellipsis are required. In this paper, we present a data management system to support flow-path related functionality for decision making and prediction modelling. Adding meta data sets and facilitating (near) real-time processing of sensor data questions are key concepts for the systems. The potential of the database framework for hydrological applications is demonstrated using different applications for the river system of Flanders. In one, the database framework is used to simulate the daily discharge for each segment within a catchment using a simple data-driven approach. The presented system is useful for numerous applications including pollution tracking, alerting and inter-sensor validation in river systems, or related networks.
Notes: Bollen, E (corresponding author), Hasselt Univ, Databases & Theoret Comp Sci Grp, Hasselt, Belgium.; Bollen, E (corresponding author), Hasselt Univ, DSI, Hasselt, Belgium.; Bollen, E (corresponding author), Flemish Inst Technol Res, Mol, Belgium.
erik.bollen@uhasselt.be
Keywords: data driven modelling;IoTrecursive querying;relational databases;river monitoring;water management
Document URI: http://hdl.handle.net/1942/36379
DOI: 10.2166/ws.2021.433
ISI #: 000729755100001
Rights: 2021 The Authors Open access
Category: A1
Type: Journal Contribution
Validations: ecoom 2022
Appears in Collections:Research publications

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