Estimating sampling and analysis uncertainties to assess the fitness for purpose of a water quality monitoring network

Guigues, Nathalie; Désenfant, Michèle; Lalere, Béatrice; Vaslin-Reimann, Sophie; Eyl, Damien; Mansuit, Philippe; Hance, Emmanuel

doi:10.1007/s00769-015-1186-4

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…The extent of uncertainty is attributable to particular sensors and is the result of the uncertainty of electrical or chemical processes that takes place within the sensor, as well as the instability of its environment or procedures [17]. There are several ways of assessing uncertainty that are based on using reference sources, environments, and protocols [18] to estimate the probability distribution function of measurements. The standard deviation is then taken as a measure of uncertainty.…”

Section: State-of-the-art-the Problem Of Measurement Uncertainty In Imentioning

confidence: 99%

Trust-Based Model for the Assessment of the Uncertainty of Measurements in Hybrid IoT Networks

Cofta

Orłowski

Lebiedź

2020

Sensors

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The aim of this paper is to introduce a NUT model (NUT: network-uncertainty-trust) that aids the decrease of the uncertainty of measurements in autonomous hybrid Internet of Things sensor networks. The problem of uncertainty in such networks is a consequence of various operating conditions and varied quality of measurement nodes, making statistical approach less successful. This paper presents a model for decreasing the uncertainty through the use of socially inspired metaphors of reputation, trust, and confidence that are the untapped latent information. The model described in the paper shows how the individual reputation of each node can be assessed on the basis of opinions provided by other nodes of the hybrid measurement network, and that this method allows to assess the extent of uncertainty the node introduces to the network. This, in turn, allows nodes of low uncertainty to have a greater impact on the reconstruction of values. The verification of the model, as well as examples of its applicability to air quality measurements are presented as well. Simulations demonstrate that the use of the model can decrease the uncertainty by up to 55% while using the EWMA (exponentially weighted moving average) algorithm, as compared to the reference one.

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Section: State-of-the-art-the Problem Of Measurement Uncertainty In Imentioning

confidence: 99%

Trust-Based Model for the Assessment of the Uncertainty of Measurements in Hybrid IoT Networks

Cofta

Orłowski

Lebiedź

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, although water quality is routinely monitored in many parts of the world, most records do not go back more than about 50 years and may be affected by changes in frequency, timings and (occasionally) analytical methods or analytical error (e.g. Guigues et al, 2016). There are some exceptions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Is water quality in British rivers “better than at any time since the end of the Industrial Revolution”?

Whelan

Linstead

Worrall

et al. 2022

Science of The Total Environment

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“…The results show that the sampling uncertainty of pH, conductivity, etc. can be ignored, while for parameters such as dissolved oxygen and total suspended solids, sampling contributes greatly to the measurement uncertainty (Guigues 2015). Nazeer M's team used ordinary least squares regression (OLS) and geographically weighted regression (GWR) as well as atmospheric correction images to model water quality and salinity.…”

Section: Related Workmentioning

confidence: 99%

Automatic sampling of seawater quality based on electric propulsion unmanned ship

2021

Water Science and Technology

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In many sea areas, toxic and harmful chemicals exceed the standard seriously, which not only has a very bad impact on the survival of marine organisms, but also makes the safety of edible groundwater damaged. With the continuous development of artificial intelligence and deep learning, it is the most efficient and safe method to detect seawater with unmanned ship. By processing and fusing the images transmitted by the two radars, the common advantages of the two sensors are integrated, and the comprehensiveness of the UAV's perception of the surrounding environment is improved. In order to improve the accuracy and safety of unmanned aerial vehicle (UAV) offshore operation, this study designs an electric propulsion unmanned ship and its automatic control system according to the requirements of water quality sampling. Based on the theory of small body, the model of unmanned ship with the least resistance and the best safety is designed. According to the requirements of water quality sampling in the sea area, the collection and analysis system of six elements of water quality is equipped. The Realizable k-e turbulence model is used to simulate the self recovery ability of unmanned ship under wave disturbance. Theoretically, the unmanned ship can realize self righting in 4.25 s. And in the actual navigation, the unmanned ship can effectively avoid obstacles, and the basic information of sea water quality is within the specified range. The unmanned ship constructed in this study can be used as an auxiliary tool for water quality detection. By comparing with various study method, the proposed method obtains better performance.

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Estimating sampling and analysis uncertainties to assess the fitness for purpose of a water quality monitoring network

Cited by 15 publications

References 26 publications

Trust-Based Model for the Assessment of the Uncertainty of Measurements in Hybrid IoT Networks

Trust-Based Model for the Assessment of the Uncertainty of Measurements in Hybrid IoT Networks

Is water quality in British rivers “better than at any time since the end of the Industrial Revolution”?

Automatic sampling of seawater quality based on electric propulsion unmanned ship

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