EGU2020-9291, updated on 29 Apr 2024
EGU General Assembly 2020
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data driven methods for real time flood, drought and water quality monitoring: applications for Internet of Water

Brianna Pagán1,2, Nele Desmet1, Piet Seuntjens1, Erik Bollen3, and Bart Kuijpers3
Brianna Pagán et al.
  • 1Environmental Modelling Unit, Flemish Institute for Technological Research (VITO), Mol, Belgium, (
  • 2Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium (
  • 3Hasselt University, Department of Computer Science, Hasselt, Belgium (

The Internet of Water (IoW) is a large-scale permanent sensor network with 2500 small, energy-efficient wireless water quality sensors spread across Flanders, Belgium. This intelligent water management system will permanently monitor water quality and quantity in real time. Such a dense network of sensors with high temporal resolution (sub-hourly) will provide unprecedented volumes of data for drought, flood and pollution management, prediction and decisions. While traditional physical hydrological models are obvious choices for utilizing such a dataset, computational costs or limitations must be considered when working in real time decision making.

In collaboration with the Flemish Institute for Technological Research (VITO) and the University of Hasselt, we present several data mining and machine learning initiatives which support the IoW. Examples include interpolating grab sample measurements to river stretches to monitor salinity intrusion. A shallow feed forward neural network is trained on historical grab samples using physical characteristics of the river stretches (i.e. soil properties, ocean connectivity). Such a system allows for salinity monitoring without complex convection-diffusion modeling, and for estimating salinity in areas with less monitoring stations. Another highlighted project is the coupling of neural network and data assimilation schemes for water quality forecasting. A long short-term memory recurrent neural network is trained on historical water quality parameters and remotely sensed spatially distributed weather data. Using forecasted weather data, a model estimate of water quality parameters are obtained from the neural network. A Newtonian nudging data assimilation scheme further corrects the forecast leveraging previous day observations, which can aid in the correction for non-point or non-weather driven pollution influences. Calculations are supported by an optimized database system developed by the University of Hasselt which further exploits data mining techniques to estimate water movement and timing through the Flanders river network system. As geospatial data increases exponentially in both temporal and spatial resolutions, scientists and water managers must consider the tradeoff between computational resources and physical model accuracy. These type of hybrid approaches allows for near real-time analysis without computational limitations and will further support research to make communities more climate resilient.

How to cite: Pagán, B., Desmet, N., Seuntjens, P., Bollen, E., and Kuijpers, B.: Data driven methods for real time flood, drought and water quality monitoring: applications for Internet of Water, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9291,, 2020.


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