Ibáñez Civera, FJ.; García Breijo, E.; Laguarda Miró, N.; Gil Sánchez, L.; Garrigues Baixauli, J.; Romero Gil, I.; Masot Peris, R.... (2011). Artificial neural network onto eight bit microcontroller for Secchi depth calculation. Sensors and Actuators B: Chemical. 156(1):132-139. doi:10.1016/j.snb.2011.04.001
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/47017
Title:
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Artificial neural network onto eight bit microcontroller for Secchi depth calculation
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Author:
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Ibáñez Civera, Francisco Javier
García Breijo, Eduardo
Laguarda Miró, Nicolás
Gil Sánchez, Luís
Garrigues Baixauli, José
Romero Gil, Inmaculada
Masot Peris, Rafael
Alcañiz Fillol, Miguel
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UPV Unit:
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Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Universitat Politècnica de València. Departamento de Ingeniería Química y Nuclear - Departament d'Enginyeria Química i Nuclear
Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Universitat Politècnica de València. Instituto de Reconocimiento Molecular y Desarrollo Tecnológico - Institut de Reconeixement Molecular i Desenvolupament Tecnològic
Universitat Politècnica de València. Instituto Universitario de Ingeniería del Agua y del Medio Ambiente - Institut Universitari d'Enginyeria de l'Aigua i Medi Ambient
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Issued date:
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Abstract:
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In this paper we present a model to predict Secchi depth in water bodies by means of an artificial neural network application onto eight bit microcontroller. Water turbidity data were collected by both a Secchi disk and a ...[+]
In this paper we present a model to predict Secchi depth in water bodies by means of an artificial neural network application onto eight bit microcontroller. Water turbidity data were collected by both a Secchi disk and a new patented device (named LUZEX) that uses commercial photodiodes with not monochromatic sensitive band as a basis to perform "in situ" measurements for sunlight extinction coefficients. In order to have a wide range of turbidity data three different water bodies were selected to do the measurements. The developed neural network model is able to relate well the data obtained by these methods and the obtained value for regression coefficient (R) is 0.998. Secchi depth measure is a reference method to determine turbidity in continental and coastal water bodies, especially in the Mediterranean Sea region, but sometimes there are particular cases that makes difficult the use of the Secchi disk (e.g. shallow water bodies), the authors propose LUZEX as a substitute for Secchi disk when it is difficult or impossible to use. © 2011 Elsevier B.V.
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Subjects:
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Artificial neural network
,
Secchi depth
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Secchi disk
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Sunlight extinction coefficient
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Turbidity
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Water quality
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Coastal water bodies
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Commercial photodiodes
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Extinction coefficients
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In-situ
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Mediterranean sea
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Neural network model
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Reference method
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Regression coefficient
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Shallow water bodies
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Water turbidity
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Waterbodies
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Microcontrollers
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Water analysis
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Neural networks
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Copyrigths:
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Cerrado |
Source:
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Sensors and Actuators B: Chemical. (issn:
0925-4005
)
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DOI:
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10.1016/j.snb.2011.04.001
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Publisher:
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Elsevier
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Publisher version:
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http://dx.doi.org/10.1016/j.snb.2011.04.001
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Type:
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Artículo
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