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. 2016 Jul 28;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

Ekaterini Hadjisolomou  1 , Konstantinos Stefanidis  2   3 , George Papatheodorou  4 , Evanthia Papastergiadou  5
Affiliations

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

Ekaterini Hadjisolomou et al. Int J Environ Res Public Health. .

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.

Keywords: Artificial Neural Network; environmental parameter; eutrophication; lake; “PaD2” method; “PaD” method.

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Figures

Figure 1
Figure 1
Satellite map of the study area, where Ioannina city is located northwestern. The urban area of Ioannina city is observed in the west side of the lake.
Figure 2
Figure 2
Relative contribution of the input parameters to the ANN model with the use of Partial Derivatives (“PaD”) sensitivity method.
Figure 3
Figure 3
Relative importance of the paired input parameters to the ANN with the use of “PaD2” sensitivity method.
Figure 4
Figure 4
Partial Derivative (D) of the ANN response to the paired interaction of the soluble reactive phosphorus (SRP) and water temperature (WT) variables.
Figure 5
Figure 5
Partial Derivative (D) of the ANN response to the paired interaction of the secchi disk (SD) and water temperature (WT) variables.
Figure 6
Figure 6
Partial Derivative (D) of the ANN response to the paired interaction of the pH and water temperature (WT) variables.
Figure 7
Figure 7
Partial Derivative (D) of the ANN response to the paired interaction of the pH and secchi disk (SD) variables.
Figure 8
Figure 8
Partial Derivative (D) of the ANN response to the paired interaction of the pH and soluble reactive phosphorus (SRP) variables.
Figure 9
Figure 9
Partial Derivative (D) of the ANN response to the paired interaction of the soluble reactive phosphorus (SRP) and dissolved inorganic nitrogen (DIN) variables.
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