A Greedy Algorithm for Optimal Sensor Placement to Estimate Salinity in Polder Networks
by
Boran Ekin Aydin 1,*
, Hugo Hagedooren 2, Martine M. Rutten 3, Joost Delsman 4, Gualbert H. P. Oude Essink 4,5, Nick van de Giesen 1 and Edo Abraham 1
Boran Ekin Aydin 1,*, Hugo Hagedooren 2, Martine M. Rutten 3, Joost Delsman 4, Gualbert H. P. Oude Essink 4,5, Nick van de Giesen 1 and Edo Abraham 1
1
Department of Water Management, Delft University of Technology, 2628 CN Delft, The Netherlands
2
HKV Consultants, P.O. Box 2120, 8203 AC Lelystad, The Netherlands
3
Engineering and Applied Sciences, Rotterdam University, 3015 GG Rotterdam, The Netherlands
4
Department of Subsurface and Groundwater, Deltares, P.O. Box 85467, 3508 Al Utrecht, The Netherlands
5
Department of Physical Geography, Utrecht University, 3584 CS Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Water 2019, 11(5), 1101; https://doi.org/10.3390/w11051101
Received: 24 April 2019 / Revised: 17 May 2019 / Accepted: 21 May 2019 / Published: 27 May 2019
(This article belongs to the Section Water Resources Management and Governance)
We present a systematic approach for salinity sensor placement in a polder network, where the objective is to estimate the unmeasured salinity levels in the main polder channels. We formulate this problem as optimization of the estimated salinity levels using root mean square error (RMSE) as the “goodness of fit” measure. Starting from a hydrodynamic and salt transport model of the Lissertocht catchment (a low-lying polder in the Netherlands), we use principal component analysis (PCA) to produce a low-order PCA model of the salinity distribution in the catchment. This model captures most of the relevant salinity dynamics and is capable of reconstructing the spatial and temporal salinity variation of the catchment. Just using three principal components (explaining 93% of the variance of the dataset) for the low-order PCA model, three optimally placed sensors with a greedy algorithm make the placement robust for modeling and measurement errors. The performance of the sensor placement for salinity reconstruction is evaluated against the detailed hydrodynamic and salt transport model and is shown to be close to the global optimum found by an exhaustive search with a RMSE of 82.2 mg/L.
View Full-Text
Keywords:
polder; salinization; principal component analysis; greedy algorithm; flushing control; sensor
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Aydin, B.E.; Hagedooren, H.; Rutten, M.M.; Delsman, J.; Oude Essink, G.H.P.; van de Giesen, N.; Abraham, E. A Greedy Algorithm for Optimal Sensor Placement to Estimate Salinity in Polder Networks. Water 2019, 11, 1101.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
