Entropy Applications to Water Monitoring Network Design: A Review

Keum, Jongho; Kornelsen, Kurt C.; Leach, James M.; Coulibaly, Paulin

doi:10.3390/e19110613

Cited by 54 publications

(25 citation statements)

References 70 publications

(138 reference statements)

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“…As second measure to evaluate the information loss when stations are removed from the network, we use a kriging based geostatistical approach (Adhikary et al, 2015;Keum et al, 2017). Kriging is an optimal surface interpolation technique assuming that the variance in a sample of observations depends on their distance (Adhikary et al, 2015).…”

Section: Relative Kriging Errormentioning

confidence: 99%

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

Agarwal¹,

Marwan²,

Rathinasamy³

et al. 2018

Preprint

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AbstractsHydrometric networks play a vital role in providing information for decision-making in water resources management. They should be set up optimally to provide as much and as accurate information as possible, and at the same time, be cost-effective.We propose a new measure, based on complex network analysis, to support the design and redesign of hydrometric station 15 networks. The science of complex networks is a relatively young field and has gained significant momentum in the last years in different areas such as brain networks, social networks, technological networks or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node ranking measure, the weighted degree-betweenness, to evaluate the importance of nodes in a network. It is compared to previously proposed measures on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to 20 check its applicability in the optimal design of hydrometric networks. The proposed measure is evaluated using the decline rate of network efficiency and the kriging error. The results suggest that it effectively quantifies the importance of rain stations.The new measure is very useful in identifying influential stations which need high attention and expendable stations which can be removed without much loss of information provided by the station network.

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Section: Relative Kriging Errormentioning

confidence: 99%

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

Agarwal¹,

Marwan²,

Rathinasamy³

et al. 2018

Preprint

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“…A successful environmental water quality monitoring program relies on the following main elements [26]; identification of the monitoring objectives, national and EU institutional setting, preliminary surveys, monitoring design (selection of representative sampling sites and frequencies, and water quality variables), adjustment to budget and operational restrictions, design of field protocols, field monitoring operations, laboratory activities, quality-assurance procedures, data management and analysis, and product development and reporting. Based on the review concerning the design of water quality monitoring networks, there is a variety of methodological approaches, e.g., statistical, multicriteria decision-making, entropy-based and fuzzy logic approaches, or a combination of the above (e.g., [27][28][29][30][31][32][33][34]). These methodologies are usually difficult to be applied by not qualified personnel and require long and reliable time-series [35]; therefore, they are usually used in evaluation and optimization of an existing monitoring program, rather than in the initial design of water quality monitoring network.…”

Section: Introductionmentioning

confidence: 99%

Designing the National Network for Automatic Monitoring of Water Quality Parameters in Greece

Mentzafou

Panagopoulos

Dimitriou

2019

Water

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Water quality indices that describe the status of water are commonly used in freshwater vulnerability assessment. The design of river water quality monitoring programs has always been a complex process and despite the numerous methodologies employed by experts, there is still no generally accepted, holistic and practical approach to support all the phases and elements related. Here, a Geographical Information System (GIS)-based multicriteria decision analysis approach was adopted so as to contribute to the design of the national network for monitoring of water quality parameters in Greece that will additionally fulfill the urgent needs for an operational, real-time monitoring of the water resources. During this cost-effective and easily applied procedure the high priority areas were defined by taking into consideration the most important conditioning factors that impose pressures on rivers and the special conditions that increase the need for monitoring locally. The areas of increased need for automatic monitoring of water quality parameters are highlighted and the output map is validated. The sites in high priority areas are proposed for the installation of automatic monitoring stations and the installation and maintenance budget is presented. Finally, the proposed network is contrasted with the current automatic monitoring network in Greece.

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“…Some of the examples include: water quality monitoring [7][8][9], water level monitoring [10], stream flow monitoring [11,12], fluid dynamic applications [13,14] and predicting the dynamic variations of a groundwater system [15]. Comprehensive reviews can be found in [16][17][18] for different water systems. Entropy theory developed by [19] is used for water quality monitoring networks optimization in rivers [7,20,21], in a bay [22], in sewer systems [23,24] and groundwater [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Greedy Algorithm for Optimal Sensor Placement to Estimate Salinity in Polder Networks

Aydin

Hagedooren

Rutten

et al. 2019

Water

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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.

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Entropy Applications to Water Monitoring Network Design: A Review

Cited by 54 publications

References 70 publications

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

Optimal Design of Hydrometric Station Networks Based on Complex Network Analysis

Designing the National Network for Automatic Monitoring of Water Quality Parameters in Greece

A Greedy Algorithm for Optimal Sensor Placement to Estimate Salinity in Polder Networks

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