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Article

Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks

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Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 30 Arch. Kyprianos Street, Limassol 3036, Cyprus
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Department of Biology, School of Natural Sciences, University of Patras, University Campus Rio, 26500 Patra, Greece
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Hellenic Centre for Marine Research (HCMR), Institute of Marine Biological Resources and Inland Waters, 46.7 km of Athens—Sounio Ave., 19013 Anavyssos, Greece
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Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras, 26504 Patra, Greece
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Author to whom correspondence should be addressed.
Academic Editor: Peter Goethals
Water 2021, 13(11), 1590; https://doi.org/10.3390/w13111590
Received: 22 April 2021 / Revised: 28 May 2021 / Accepted: 1 June 2021 / Published: 4 June 2021
Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control. View Full-Text
Keywords: data scarcity; k-fold cross validation; artificial neural network; eutrophication data scarcity; k-fold cross validation; artificial neural network; eutrophication
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MDPI and ACS Style

Hadjisolomou, E.; Stefanidis, K.; Herodotou, H.; Michaelides, M.; Papatheodorou, G.; Papastergiadou, E. Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks. Water 2021, 13, 1590. https://doi.org/10.3390/w13111590

AMA Style

Hadjisolomou E, Stefanidis K, Herodotou H, Michaelides M, Papatheodorou G, Papastergiadou E. Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks. Water. 2021; 13(11):1590. https://doi.org/10.3390/w13111590

Chicago/Turabian Style

Hadjisolomou, Ekaterini, Konstantinos Stefanidis, Herodotos Herodotou, Michalis Michaelides, George Papatheodorou, and Eva Papastergiadou. 2021. "Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks" Water 13, no. 11: 1590. https://doi.org/10.3390/w13111590

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