Modelling of ecological status of Polish lakes using deep learning techniques.
Artificial neural network
Biological indices
Macrophytes
Phytobenthos
Phytoplankton
Water quality
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Feb 2021
Feb 2021
Historique:
received:
04
06
2020
accepted:
03
09
2020
pubmed:
24
9
2020
medline:
29
1
2021
entrez:
23
9
2020
Statut:
ppublish
Résumé
Since 2000, after the Water Framework Directive came into force, aquatic ecosystems' bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered comprehensive hydrobiological databases. The amount of available data increases with each subsequent year of monitoring, and the efficient analysis of these data requires the use of proper mathematical tools. Our study challenges the comparison of the modelling potential between four indices for the ecological status assessment of lakes based on three groups of aquatic organisms, i.e. phytoplankton, phytobenthos and macrophytes. One of the deep learning techniques, artificial neural networks, has been used to predict values of four biological indices based on the limited set of the physicochemical parameters of water. All analyses were conducted separately for lakes with various stratification regimes as they function differently. The best modelling quality in terms of high values of coefficients of determination and low values of the normalised root mean square error was obtained for chlorophyll a followed by phytoplankton multimetric. A lower degree of fit was obtained in the networks for macrophyte index, and the poorest model quality was obtained for phytobenthos index. For all indices, modelling quality for non-stratified lakes was higher than this for stratified lakes, giving a higher percentage of variance explained by the networks and lower values of errors. Sensitivity analysis showed that among physicochemical parameters, water transparency (Secchi disk reading) exhibits the strongest relationship with the ecological status of lakes derived by phytoplankton and macrophytes. At the same time, all input variables indicated a negligible impact on phytobenthos index. In this way, different explanations of the relationship between biological and trophic variables were revealed.
Identifiants
pubmed: 32964383
doi: 10.1007/s11356-020-10731-1
pii: 10.1007/s11356-020-10731-1
pmc: PMC7838144
doi:
Substances chimiques
Chlorophyll A
YF5Q9EJC8Y
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5383-5397Subventions
Organisme : Narodowa Agencja Wymiany Akademickiej (PL)
ID : PPN/BEK/2018/1/00401
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