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Int. J. Environ. Res. Public Health 2016, 13(8), 764; doi:10.3390/ijerph13080764

Assessing the Contribution of the Environmental Parameters to Eutrophication with the Use of the “PaD” and “PaD2” Methods in a Hypereutrophic Lake

1
Laboratory of Marine Geology and Physical Oceanography, Department of Geology, Patras University, Patras 26504, Greece
2
Department of Biology, University of Patras-University Campus Rio, Patras 26500, Greece
3
Sector of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Athens 15780, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Miklas Scholz
Received: 17 June 2016 / Revised: 20 July 2016 / Accepted: 22 July 2016 / Published: 28 July 2016
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Abstract

Lake Pamvotis (Greece) is a shallow hypereutrophic lake with a natural tendency to eutrophication. Several restoration measures were applied, but with no long-term success. To examine the causes for this an Artificial Neural Network (ANN) was created in order to simulate the chlorophyll-a (Chl-a) levels and to investigate the role of the associated environmental parameters. The ANN managed to simulate with good correlation the simulated Chl-a and can be considered as a reliable predictor. The relative importance of the environmental parameters to the simulated Chl-a was calculated with the use of the “Partial Derivatives” (“PaD”) sensitivity method. The water temperature (WT) and soluble reactive phosphorus (SRP) had the highest relative importance, with values of 50% and 17%, respectively. The synergistic effect of the paired parameters was calculated with the use of the “PaD2” algorithm. The SRP-WT paired parameter was the most influential, with a relative contribution of 22%. The ANN showed that Lake Pamvotis is prone to suffer the effects of climatic change, because of the major contribution of WT. The ANN also revealed that combined nutrients reduction would improve water quality status. The ANN findings can act as an advisory tool regarding any restoration efforts. View Full-Text
Keywords: Artificial Neural Network; eutrophication; lake; environmental parameter; “PaD” method; “PaD2” method Artificial Neural Network; eutrophication; lake; environmental parameter; “PaD” method; “PaD2” method
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MDPI and ACS Style

Hadjisolomou, E.; Stefanidis, K.; Papatheodorou, G.; Papastergiadou, E. Assessing the Contribution of the Environmental Parameters to Eutrophication with the Use of the “PaD” and “PaD2” Methods in a Hypereutrophic Lake. Int. J. Environ. Res. Public Health 2016, 13, 764.

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