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Applicability of a processes-based model and artificial neural networks to estimate the concentration of major ions in rivers

Nhantumbo, Clemêncio LU ; Carvalho, Frede ; Uvo, Cintia LU ; Larsson, Rolf LU and Larson, Magnus LU (2018) In Journal of Geochemical Exploration 193. p.32-40
Abstract

Modelling is an alternative solution to reduce the cost of water quality monitoring. Commonly, concentration of pollutants is estimated based on limited sampling information. Concentration of ions in rivers can be estimated using modelling strategies that involve statistics and artificial intelligence as well as the understanding of physical processes. Therefore, the performance of feedforward neural networks that employs the Levenberg-Marquardt optimization method was compared to the PPBM recently proposed. Both ANN and PPBM were used to estimate the concentration of major ions (Na+, K+, Mg2+, Ca2+, HCO3
, SO4
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Modelling is an alternative solution to reduce the cost of water quality monitoring. Commonly, concentration of pollutants is estimated based on limited sampling information. Concentration of ions in rivers can be estimated using modelling strategies that involve statistics and artificial intelligence as well as the understanding of physical processes. Therefore, the performance of feedforward neural networks that employs the Levenberg-Marquardt optimization method was compared to the PPBM recently proposed. Both ANN and PPBM were used to estimate the concentration of major ions (Na+, K+, Mg2+, Ca2+, HCO3
, SO4
2−, Cl, and NO3
) in river water based on pH, alkalinity, and temperature. Root-mean-square error and Pearson correlation coefficient (R) together with its p-value were used to evaluate the quality of results of both models. The ANN model provides better estimates compared to the PPBM in most cases. However, the PPBM has the possibility to evaluate its predictions by using the difference between the estimated and measured electrical conductivity. If the predictions are not good the PPBM can be recalibrated, whereas the ANN model is limited in this respect. Another disadvantage of ANN models is that they are developed based on historical data and if limited data are available, such models cannot be used. This latter disadvantage makes the PPBM superior in developing countries, where often little or no consistent historical data exist.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Major ions, Model comparison and neural networks, River monitoring, Water quality
in
Journal of Geochemical Exploration
volume
193
pages
9 pages
publisher
Elsevier
external identifiers
  • scopus:85049791661
ISSN
0375-6742
DOI
10.1016/j.gexplo.2018.07.003
language
English
LU publication?
yes
id
d6121140-9c4a-4811-98c9-63e768dfd4f9
date added to LUP
2018-09-06 12:10:37
date last changed
2020-01-13 00:54:57
@article{d6121140-9c4a-4811-98c9-63e768dfd4f9,
  abstract     = {<p>Modelling is an alternative solution to reduce the cost of water quality monitoring. Commonly, concentration of pollutants is estimated based on limited sampling information. Concentration of ions in rivers can be estimated using modelling strategies that involve statistics and artificial intelligence as well as the understanding of physical processes. Therefore, the performance of feedforward neural networks that employs the Levenberg-Marquardt optimization method was compared to the PPBM recently proposed. Both ANN and PPBM were used to estimate the concentration of major ions (Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, HCO<sub>3</sub><br>
                            <sup>−</sup>, SO<sub>4</sub><br>
                            <sup>2−</sup>, Cl<sup>−</sup>, and NO<sub>3</sub><br>
                            <sup>−</sup>) in river water based on pH, alkalinity, and temperature. Root-mean-square error and Pearson correlation coefficient (R) together with its p-value were used to evaluate the quality of results of both models. The ANN model provides better estimates compared to the PPBM in most cases. However, the PPBM has the possibility to evaluate its predictions by using the difference between the estimated and measured electrical conductivity. If the predictions are not good the PPBM can be recalibrated, whereas the ANN model is limited in this respect. Another disadvantage of ANN models is that they are developed based on historical data and if limited data are available, such models cannot be used. This latter disadvantage makes the PPBM superior in developing countries, where often little or no consistent historical data exist.</p>},
  author       = {Nhantumbo, Clemêncio and Carvalho, Frede and Uvo, Cintia and Larsson, Rolf and Larson, Magnus},
  issn         = {0375-6742},
  language     = {eng},
  month        = {10},
  pages        = {32--40},
  publisher    = {Elsevier},
  series       = {Journal of Geochemical Exploration},
  title        = {Applicability of a processes-based model and artificial neural networks to estimate the concentration of major ions in rivers},
  url          = {http://dx.doi.org/10.1016/j.gexplo.2018.07.003},
  doi          = {10.1016/j.gexplo.2018.07.003},
  volume       = {193},
  year         = {2018},
}