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A greedy algorithm for optimal sensor placement to estimate salinity in polder networks. / Aydin, Boran Ekin; Hagedooren, Hugo; Rutten, Martine M.; Delsman, Joost; Essink, Gualbert H.P.Oude; van de Giesen, Nick; Abraham, Edo.

In: Water (Switzerland), Vol. 11, No. 5, 1101, 2019.

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Aydin, Boran Ekin ; Hagedooren, Hugo ; Rutten, Martine M. ; Delsman, Joost ; Essink, Gualbert H.P.Oude ; van de Giesen, Nick ; Abraham, Edo. / A greedy algorithm for optimal sensor placement to estimate salinity in polder networks. In: Water (Switzerland). 2019 ; Vol. 11, No. 5.

BibTeX

@article{286aba25e3644b72a1ba102a62f8c143,
title = "A greedy algorithm for optimal sensor placement to estimate salinity in polder networks",
abstract = "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.",
keywords = "Flushing control, Greedy algorithm, Polder, Principal component analysis, Salinization, Sensor",
author = "Aydin, {Boran Ekin} and Hugo Hagedooren and Rutten, {Martine M.} and Joost Delsman and Essink, {Gualbert H.P.Oude} and {van de Giesen}, Nick and Edo Abraham",
year = "2019",
doi = "10.3390/w11051101",
language = "English",
volume = "11",
journal = "Water",
issn = "2073-4441",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "5",

}

RIS

TY - JOUR

T1 - A greedy algorithm for optimal sensor placement to estimate salinity in polder networks

AU - Aydin, Boran Ekin

AU - Hagedooren, Hugo

AU - Rutten, Martine M.

AU - Delsman, Joost

AU - Essink, Gualbert H.P.Oude

AU - van de Giesen, Nick

AU - Abraham, Edo

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Flushing control

KW - Greedy algorithm

KW - Polder

KW - Principal component analysis

KW - Salinization

KW - Sensor

UR - http://www.scopus.com/inward/record.url?scp=85066332690&partnerID=8YFLogxK

U2 - 10.3390/w11051101

DO - 10.3390/w11051101

M3 - Article

VL - 11

JO - Water

T2 - Water

JF - Water

SN - 2073-4441

IS - 5

M1 - 1101

ER -

ID: 54296296